diff --git a/backends/candle/src/lib.rs b/backends/candle/src/lib.rs
index 75c41333..30cf5762 100644
--- a/backends/candle/src/lib.rs
+++ b/backends/candle/src/lib.rs
@@ -13,7 +13,7 @@ use crate::compute_cap::{
 use crate::models::{
     BertConfig, BertModel, DistilBertConfig, DistilBertModel, GTEConfig, GTEModel, JinaBertModel,
     JinaCodeBertModel, MPNetConfig, MPNetModel, MistralConfig, Model, ModernBertConfig,
-    ModernBertModel, NomicBertModel, NomicConfig, Qwen2Config, Qwen3Config,
+    ModernBertModel, NomicBertModel, NomicConfig, Qwen2Config, Qwen3Config, Qwen3Model,
 };
 #[cfg(feature = "cuda")]
 use crate::models::{
@@ -275,10 +275,10 @@ impl CandleBackend {
                 "Qwen2 is only supported on Cuda devices in fp16 with flash attention enabled"
                     .to_string(),
             )),
-            (Config::Qwen3(_), Device::Cpu | Device::Metal(_)) => Err(BackendError::Start(
-                "Qwen3 is only supported on Cuda devices in fp16 with flash attention enabled"
-                    .to_string(),
-            )),
+            (Config::Qwen3(config), Device::Cpu | Device::Metal(_)) => {
+                tracing::info!("Starting Qwen3 model on {:?}", device);
+                Ok(Box::new(Qwen3Model::load(vb, &config, model_type).s()?))
+            },
             (Config::MPNet(config), _) => {
                 tracing::info!("Starting MPNet model on {:?}", device);
                 Ok(Box::new(MPNetModel::load(vb, &config, model_type).s()?))
diff --git a/backends/candle/src/models/mod.rs b/backends/candle/src/models/mod.rs
index 8d610251..0d4d5506 100644
--- a/backends/candle/src/models/mod.rs
+++ b/backends/candle/src/models/mod.rs
@@ -59,7 +59,7 @@ pub use modernbert::{ModernBertConfig, ModernBertModel};
 pub use mpnet::{MPNetConfig, MPNetModel};
 pub use nomic::{NomicBertModel, NomicConfig};
 pub use qwen2::Qwen2Config;
-pub use qwen3::Qwen3Config;
+pub use qwen3::{Qwen3Config, Qwen3Model};
 use text_embeddings_backend_core::Batch;
 
 #[cfg(feature = "cuda")]
diff --git a/backends/candle/src/models/qwen3.rs b/backends/candle/src/models/qwen3.rs
index 7bc06db9..be8a421e 100644
--- a/backends/candle/src/models/qwen3.rs
+++ b/backends/candle/src/models/qwen3.rs
@@ -1,5 +1,9 @@
-use crate::layers::HiddenAct;
+use crate::layers::{apply_rotary, get_cos_sin, get_inv_freqs, HiddenAct, Linear, RMSNorm};
+use crate::models::Model;
+use candle::{Device, IndexOp, Result, Tensor, D};
+use candle_nn::{Embedding, Module, VarBuilder};
 use serde::Deserialize;
+use text_embeddings_backend_core::{Batch, ModelType, Pool};
 
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub struct Qwen3Config {
@@ -18,3 +22,577 @@ pub struct Qwen3Config {
     pub sliding_window: Option<usize>,
     pub use_sliding_window: bool,
 }
+
+struct Qwen3Attention {
+    q_proj: Linear,
+    k_proj: Linear,
+    v_proj: Linear,
+    o_proj: Linear,
+    q_norm: RMSNorm,
+    k_norm: RMSNorm,
+
+    num_attention_heads: usize,
+    num_key_value_heads: usize,
+    attention_head_size: usize,
+
+    softmax_scale: f64,
+
+    span: tracing::Span,
+}
+
+impl Qwen3Attention {
+    pub fn load(vb: VarBuilder, config: &Qwen3Config) -> Result<Self> {
+        if config.use_sliding_window {
+            candle::bail!("Sliding window is not supported for Qwen3");
+        }
+
+        let num_attention_heads = config.num_attention_heads;
+        let attention_head_size = config
+            .head_dim
+            .unwrap_or(config.hidden_size / config.num_attention_heads);
+        let num_key_value_heads = config.num_key_value_heads;
+        let hidden_size = config.hidden_size;
+
+        let query_weight = vb.pp("q_proj").get(
+            (num_attention_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let query_bias = if config.attention_bias {
+            Some(vb.pp("q_proj").get(num_attention_heads * attention_head_size, "bias")?)
+        } else {
+            None
+        };
+        let q_proj = Linear::new(query_weight, query_bias, None);
+
+        let key_weight = vb.pp("k_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let key_bias = if config.attention_bias {
+            Some(
+                vb.pp("k_proj")
+                    .get(num_key_value_heads * attention_head_size, "bias")?,
+            )
+        } else {
+            None
+        };
+        let k_proj = Linear::new(key_weight, key_bias, None);
+
+        let value_weight = vb.pp("v_proj").get(
+            (num_key_value_heads * attention_head_size, hidden_size),
+            "weight",
+        )?;
+        let value_bias = if config.attention_bias {
+            Some(
+                vb.pp("v_proj")
+                    .get(num_key_value_heads * attention_head_size, "bias")?,
+            )
+        } else {
+            None
+        };
+        let v_proj = Linear::new(value_weight, value_bias, None);
+
+        let o_proj_weight = vb.pp("o_proj").get(
+            (hidden_size, num_attention_heads * attention_head_size),
+            "weight",
+        )?;
+        let o_proj = Linear::new(o_proj_weight, None, None);
+
+        let q_norm = RMSNorm::load(vb.pp("q_norm"), attention_head_size, config.rms_norm_eps)?;
+        let k_norm = RMSNorm::load(vb.pp("k_norm"), attention_head_size, config.rms_norm_eps)?;
+
+        let softmax_scale = 1.0 / (attention_head_size as f64).sqrt();
+
+        Ok(Self {
+            q_proj,
+            k_proj,
+            v_proj,
+            o_proj,
+            q_norm,
+            k_norm,
+            num_attention_heads,
+            num_key_value_heads,
+            attention_head_size,
+            softmax_scale,
+            span: tracing::span!(tracing::Level::TRACE, "attention"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        attention_bias: Option<&Tensor>,
+        cos: &Tensor,
+        sin: &Tensor,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let q = self.q_proj.forward(hidden_states)?;
+        let k = self.k_proj.forward(hidden_states)?;
+        let v = self.v_proj.forward(hidden_states)?;
+
+        let input_dims = hidden_states.dims();
+        let input_shape = &input_dims[..input_dims.len() - 1];
+
+        // Reshape to [batch, seq_len, heads, head_dim]
+        let q = q.reshape(
+            [
+                input_shape,
+                &[self.num_attention_heads, self.attention_head_size],
+            ]
+            .concat(),
+        )?;
+        let k = k.reshape(
+            [
+                input_shape,
+                &[self.num_key_value_heads, self.attention_head_size],
+            ]
+            .concat(),
+        )?;
+        let v = v.reshape(
+            [
+                input_shape,
+                &[self.num_key_value_heads, self.attention_head_size],
+            ]
+            .concat(),
+        )?;
+
+        // Apply q_norm and k_norm
+        let (q, _res) = self.q_norm.forward(&q, None)?;
+        let (k, _res) = self.k_norm.forward(&k, None)?;
+
+        // Transpose to [batch, heads, seq_len, head_dim] for compatibility with apply_rotary
+        let q = q.transpose(1, 2)?;
+        let k = k.transpose(1, 2)?;
+        let v = v.transpose(1, 2)?;
+
+        // Apply rotary embeddings
+        let q = apply_rotary(&q, cos, sin, self.attention_head_size)?;
+        let k = apply_rotary(&k, cos, sin, self.attention_head_size)?;
+
+        // For simplicity, expand k and v to match number of q heads if needed (GQA)
+        let k = if self.num_key_value_heads != self.num_attention_heads {
+            let repeat_factor = self.num_attention_heads / self.num_key_value_heads;
+            let (b, h, s, d) = k.shape().dims4()?;
+            let k = k.unsqueeze(2)?.expand((b, h, repeat_factor, s, d))?;
+            k.reshape((b, h * repeat_factor, s, d))?
+        } else {
+            k
+        };
+        let v = if self.num_key_value_heads != self.num_attention_heads {
+            let repeat_factor = self.num_attention_heads / self.num_key_value_heads;
+            let (b, h, s, d) = v.shape().dims4()?;
+            let v = v.unsqueeze(2)?.expand((b, h, repeat_factor, s, d))?;
+            v.reshape((b, h * repeat_factor, s, d))?
+        } else {
+            v
+        };
+
+        let attention_scores = q.matmul(&k.t()?)?;
+        let mut attention_scores = (attention_scores * self.softmax_scale)?;
+
+        if let Some(attention_bias) = attention_bias {
+            attention_scores = attention_scores.add(attention_bias)?;
+        }
+
+        let attention_probs = candle_nn::ops::softmax_last_dim(&attention_scores)?;
+        let context_layer = attention_probs.matmul(&v.contiguous()?)?;
+
+        // Transpose back and flatten: [batch, heads, seq_len, head_dim] -> [batch, seq_len, hidden_size]
+        let context_layer = context_layer.transpose(1, 2)?.flatten_from(D::Minus2)?;
+
+        self.o_proj.forward(&context_layer)
+    }
+}
+
+struct Qwen3MLP {
+    gate_up_proj: Linear,
+    down_proj: Linear,
+
+    intermediate_size: usize,
+
+    span: tracing::Span,
+}
+
+impl Qwen3MLP {
+    pub fn load(vb: VarBuilder, config: &Qwen3Config) -> Result<Self> {
+        let intermediate_size = config.intermediate_size;
+
+        let gate_proj_weight = vb
+            .pp("gate_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let up_proj_weight = vb
+            .pp("up_proj")
+            .get((intermediate_size, config.hidden_size), "weight")?;
+
+        let gate_up_proj_weight = Tensor::cat(&[&gate_proj_weight, &up_proj_weight], 0)?;
+        let gate_up_proj = Linear::new(gate_up_proj_weight, None, None);
+
+        let down_proj_weight = vb
+            .pp("down_proj")
+            .get((config.hidden_size, intermediate_size), "weight")?;
+        let down_proj = Linear::new(down_proj_weight, None, None);
+
+        Ok(Self {
+            gate_up_proj,
+            down_proj,
+            intermediate_size,
+            span: tracing::span!(tracing::Level::TRACE, "mlp"),
+        })
+    }
+
+    pub fn forward(&self, hidden_states: &Tensor) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let gate_up_states = self.gate_up_proj.forward(hidden_states)?;
+        let gate_states = gate_up_states.narrow(D::Minus1, 0, self.intermediate_size)?;
+        let up_states = gate_up_states.narrow(D::Minus1, self.intermediate_size, self.intermediate_size)?;
+
+        let gate_states = gate_states.silu()?;
+        self.down_proj.forward(&(gate_states * up_states)?)
+    }
+}
+
+struct Qwen3Layer {
+    attention: Qwen3Attention,
+    mlp: Qwen3MLP,
+    input_layer_norm: RMSNorm,
+    post_attention_layer_norm: RMSNorm,
+
+    span: tracing::Span,
+}
+
+impl Qwen3Layer {
+    pub fn load(vb: VarBuilder, config: &Qwen3Config) -> Result<Self> {
+        let attention = Qwen3Attention::load(vb.pp("self_attn"), config)?;
+        let mlp = Qwen3MLP::load(vb.pp("mlp"), config)?;
+
+        let input_layer_norm = RMSNorm::load(
+            vb.pp("input_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+        let post_attention_layer_norm = RMSNorm::load(
+            vb.pp("post_attention_layernorm"),
+            config.hidden_size,
+            config.rms_norm_eps,
+        )?;
+
+        Ok(Self {
+            attention,
+            mlp,
+            input_layer_norm,
+            post_attention_layer_norm,
+            span: tracing::span!(tracing::Level::TRACE, "layer"),
+        })
+    }
+
+    pub fn forward(
+        &self,
+        hidden_states: &Tensor,
+        attention_bias: Option<&Tensor>,
+        cos: &Tensor,
+        sin: &Tensor,
+    ) -> Result<Tensor> {
+        let _enter = self.span.enter();
+
+        let (normed_hidden_states, res) = self.input_layer_norm.forward(hidden_states, None)?;
+        let attn_output = self.attention.forward(&normed_hidden_states, attention_bias, cos, sin)?;
+        let (normed_attn_res_output, attn_res) = self.post_attention_layer_norm.forward(&attn_output, Some(&res))?;
+        let mlp_output = self.mlp.forward(&normed_attn_res_output)?;
+
+        // Add residual connections
+        let output = (&mlp_output + &attn_res)?;
+        Ok(output)
+    }
+}
+
+pub struct Qwen3Model {
+    embeddings: Embedding,
+    layers: Vec<Qwen3Layer>,
+    norm: RMSNorm,
+    rotary_cache: (Tensor, Tensor),
+    rotary_dim: usize,
+    pool: Pool,
+    pub device: Device,
+    num_attention_heads: usize,
+
+    span: tracing::Span,
+}
+
+impl Qwen3Model {
+    pub fn load(vb: VarBuilder, config: &Qwen3Config, model_type: ModelType) -> Result<Self> {
+        let pool = match model_type {
+            ModelType::Classifier => {
+                candle::bail!("`classifier` model type is not supported for Qwen3")
+            }
+            ModelType::Embedding(pool) => pool,
+        };
+
+        // Handle potential "model" prefix for reranker models
+        let vb = if vb.contains_tensor("model.embed_tokens.weight") {
+            vb.pp("model")
+        } else {
+            vb
+        };
+
+        let embeddings = Embedding::new(
+            vb.pp("embed_tokens")
+                .get((config.vocab_size, config.hidden_size), "weight")?,
+            config.hidden_size,
+        );
+
+        let layers = (0..config.num_hidden_layers)
+            .map(|index| Qwen3Layer::load(vb.pp(format!("layers.{index}")), config))
+            .collect::<Result<Vec<_>>>()?;
+
+        let norm = RMSNorm::load(vb.pp("norm"), config.hidden_size, config.rms_norm_eps)?;
+
+        let rotary_dim = config
+            .head_dim
+            .unwrap_or(config.hidden_size / config.num_attention_heads);
+
+        let inv_freqs = get_inv_freqs(
+            rotary_dim,
+            config.rope_theta,
+            vb.device(),
+            None,
+        )?;
+
+        let rotary_cache = get_cos_sin(
+            config.max_position_embeddings,
+            &inv_freqs,
+            vb.dtype(),
+            true,
+        )?;
+
+        Ok(Self {
+            embeddings,
+            layers,
+            norm,
+            rotary_cache,
+            rotary_dim,
+            pool,
+            device: vb.device().clone(),
+            num_attention_heads: config.num_attention_heads,
+            span: tracing::span!(tracing::Level::TRACE, "model"),
+        })
+    }
+
+    pub fn forward(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        let _enter = self.span.enter();
+
+        let batch_size = batch.len();
+        let max_length = batch.max_length as usize;
+
+        let shape = (batch_size, max_length);
+
+        let (input_ids, position_ids, input_lengths, attention_bias, _attention_mask) =
+            if batch_size > 1 {
+                // Prepare padded batch
+                let elems = batch_size * max_length;
+
+                let mut input_ids = Vec::with_capacity(elems);
+                let mut position_ids = Vec::with_capacity(elems);
+                let mut attention_mask = Vec::with_capacity(elems);
+                let mut attention_bias = Vec::with_capacity(elems);
+                let mut input_lengths = Vec::with_capacity(batch_size);
+                let mut masking = false;
+
+                for i in 0..batch_size {
+                    let start = batch.cumulative_seq_lengths[i] as usize;
+                    let end = batch.cumulative_seq_lengths[i + 1] as usize;
+                    let seq_length = end - start;
+                    input_lengths.push(seq_length);
+
+                    // Input ids
+                    for j in start..end {
+                        input_ids.push(batch.input_ids[j]);
+                        position_ids.push(batch.position_ids[j]);
+                        attention_mask.push(1.0_f32);
+                        attention_bias.push(0.0);
+                    }
+
+                    // Pad to max_length
+                    for _ in seq_length..max_length {
+                        input_ids.push(0);
+                        position_ids.push(0);
+                        attention_mask.push(0.0_f32);
+                        attention_bias.push(f32::NEG_INFINITY);
+                        masking = true;
+                    }
+                }
+
+                let input_ids = Tensor::from_vec(input_ids, shape, &self.device)?;
+                let position_ids = Tensor::from_vec(position_ids, shape, &self.device)?;
+                let attention_mask = if masking {
+                    Some(Tensor::from_vec(attention_mask, shape, &self.device)?)
+                } else {
+                    None
+                };
+
+                let attention_bias = if masking {
+                    let attention_bias = Tensor::from_vec(
+                        attention_bias,
+                        (batch_size, 1, 1, max_length),
+                        &self.device,
+                    )?;
+                    // Broadcast once instead of at every layer
+                    let attention_bias = attention_bias
+                        .broadcast_as((
+                            batch_size,
+                            self.num_attention_heads,
+                            max_length,
+                            max_length,
+                        ))?
+                        .contiguous()?;
+                    Some(attention_bias)
+                } else {
+                    None
+                };
+
+                (input_ids, position_ids, input_lengths, attention_bias, attention_mask)
+            } else {
+                // Single sequence
+                let input_ids = Tensor::from_vec(batch.input_ids.clone(), (1, batch.input_ids.len()), &self.device)?;
+                let position_ids = Tensor::from_vec(batch.position_ids.clone(), (1, batch.position_ids.len()), &self.device)?;
+                let input_lengths = vec![batch.input_ids.len()];
+
+                (input_ids, position_ids, input_lengths, None, None)
+            };
+
+        let mut hidden_states = self.embeddings.forward(&input_ids)?;
+
+        let cos = self.rotary_cache.0.index_select(&position_ids.flatten_all()?, 0)?;
+        let sin = self.rotary_cache.1.index_select(&position_ids.flatten_all()?, 0)?;
+
+        let cos = cos.reshape((batch_size, 1, max_length, self.rotary_dim))?;
+        let sin = sin.reshape((batch_size, 1, max_length, self.rotary_dim))?;
+
+        for layer in &self.layers {
+            hidden_states = layer.forward(&hidden_states, attention_bias.as_ref(), &cos, &sin)?;
+        }
+
+        let (outputs, _) = self.norm.forward(&hidden_states, None)?;
+
+        let has_pooling_requests = !batch.pooled_indices.is_empty();
+        let has_raw_requests = !batch.raw_indices.is_empty();
+
+        let pooled_embeddings = if has_pooling_requests {
+            match self.pool {
+                Pool::Cls => {
+                    if batch_size > 1 {
+                        let pooled_indices = Tensor::from_vec(
+                            batch.pooled_indices.clone(),
+                            batch.pooled_indices.len(),
+                            &self.device,
+                        )?;
+
+                        let cls_indices = if has_raw_requests {
+                            Tensor::zeros(batch.pooled_indices.len(), candle::DType::U32, &self.device)?
+                        } else {
+                            Tensor::arange(0u32, batch_size as u32, &self.device)?
+                        };
+
+                        Some(outputs.i((&pooled_indices, &cls_indices))?)
+                    } else {
+                        Some(outputs.i((0, 0))?.unsqueeze(0)?)
+                    }
+                }
+                Pool::LastToken => {
+                    if batch_size > 1 {
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .iter()
+                            .map(|&i| {
+                                let i = i as usize;
+                                let last_token_idx = input_lengths[i] - 1;
+                                outputs.i((i, last_token_idx))?.unsqueeze(0)
+                            })
+                            .collect();
+
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        let last_idx = input_lengths[0] - 1;
+                        Some(outputs.i((0, last_idx))?.unsqueeze(0)?)
+                    }
+                }
+                Pool::Mean => {
+                    if batch_size > 1 {
+                        let results: Result<Vec<Tensor>> = batch
+                            .pooled_indices
+                            .iter()
+                            .map(|&i| {
+                                let i = i as usize;
+                                let length = input_lengths[i];
+
+                                let embeddings = outputs.i((i, ..length))?;
+                                let sum = embeddings.sum_keepdim(0)?;
+                                sum / (length as f64)
+                            })
+                            .collect();
+
+                        Some(Tensor::cat(&results?, 0)?)
+                    } else {
+                        let length = input_lengths[0] as f64;
+                        let embeddings = outputs.i((0, ..input_lengths[0]))?;
+                        Some((embeddings.sum_keepdim(0)? / length)?)
+                    }
+                }
+                Pool::Splade => {
+                    unreachable!("Splade is not supported for Qwen3");
+                }
+            }
+        } else {
+            None
+        };
+
+        // Note: L2 normalization removed to match flash implementation behavior
+        // let pooled_embeddings = if let Some(embeddings) = pooled_embeddings {
+        //     let norm = embeddings.sqr()?.sum_keepdim(D::Minus1)?.sqrt()?;
+        //     Some(embeddings.broadcast_div(&norm)?)
+        // } else {
+        //     None
+        // };
+
+        let raw_embeddings = if has_raw_requests {
+            if batch_size > 1 && has_pooling_requests {
+                let mut final_embeddings = Vec::new();
+                for &i in &batch.raw_indices {
+                    let i = i as usize;
+                    let length = input_lengths[i];
+                    final_embeddings.push(outputs.i((i, ..length))?);
+                }
+                Some(Tensor::cat(&final_embeddings, 0)?)
+            } else {
+                // Single batch or all raw requests
+                if batch_size == 1 {
+                    let length = input_lengths[0];
+                    Some(outputs.i((0, ..length))?)
+                } else {
+                    // Multiple sequences, all raw
+                    let mut all_embeddings = Vec::new();
+                    for (i, &length) in input_lengths.iter().enumerate().take(batch_size) {
+                        all_embeddings.push(outputs.i((i, ..length))?);
+                    }
+                    Some(Tensor::cat(&all_embeddings, 0)?)
+                }
+            }
+        } else {
+            None
+        };
+
+        Ok((pooled_embeddings, raw_embeddings))
+    }
+}
+
+impl Model for Qwen3Model {
+    fn is_padded(&self) -> bool {
+        true
+    }
+
+    fn embed(&self, batch: Batch) -> Result<(Option<Tensor>, Option<Tensor>)> {
+        self.forward(batch)
+    }
+}
diff --git a/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_batch.snap b/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_batch.snap
new file mode 100644
index 00000000..8455838a
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_batch.snap
@@ -0,0 +1,3076 @@
+---
+source: backends/candle/tests/test_qwen3.rs
+expression: embeddings_batch
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diff --git a/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_single.snap b/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_single.snap
new file mode 100644
index 00000000..a2facd6c
--- /dev/null
+++ b/backends/candle/tests/snapshots/test_qwen3__qwen3_cpu_single.snap
@@ -0,0 +1,1028 @@
+---
+source: backends/candle/tests/test_qwen3.rs
+expression: embeddings_single
+---
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diff --git a/backends/candle/tests/test_qwen3.rs b/backends/candle/tests/test_qwen3.rs
new file mode 100644
index 00000000..e425d667
--- /dev/null
+++ b/backends/candle/tests/test_qwen3.rs
@@ -0,0 +1,51 @@
+mod common;
+
+use crate::common::{sort_embeddings, SnapshotEmbeddings};
+use anyhow::Result;
+use common::{batch, cosine_matcher, download_artifacts, load_tokenizer};
+use text_embeddings_backend_candle::CandleBackend;
+use text_embeddings_backend_core::{Backend, ModelType, Pool};
+
+#[test]
+#[serial_test::serial]
+fn test_qwen3() -> Result<()> {
+    let model_root = download_artifacts("Qwen/Qwen3-Embedding-0.6B", None)?;
+    let tokenizer = load_tokenizer(&model_root)?;
+
+    let backend = CandleBackend::new(
+        &model_root,
+        "float32".to_string(),
+        ModelType::Embedding(Pool::LastToken),
+    )?;
+
+    let input_batch = batch(
+        vec![
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+            tokenizer.encode("Deep Learning is...", true).unwrap(),
+            tokenizer.encode("What is Deep Learning?", true).unwrap(),
+        ],
+        [0, 1, 2].to_vec(),
+        vec![],
+    );
+
+    let matcher = cosine_matcher();
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_batch)?);
+    let embeddings_batch = SnapshotEmbeddings::from(pooled_embeddings);
+    insta::assert_yaml_snapshot!("qwen3_cpu_batch", embeddings_batch, &matcher);
+
+    let input_single = batch(
+        vec![tokenizer.encode("What is Deep Learning?", true).unwrap()],
+        [0].to_vec(),
+        vec![],
+    );
+
+    let (pooled_embeddings, _) = sort_embeddings(backend.embed(input_single)?);
+    let embeddings_single = SnapshotEmbeddings::from(pooled_embeddings);
+
+    insta::assert_yaml_snapshot!("qwen3_cpu_single", embeddings_single, &matcher);
+    assert_eq!(embeddings_batch[0], embeddings_single[0]);
+    assert_eq!(embeddings_batch[2], embeddings_single[0]);
+
+    Ok(())
+} 
\ No newline at end of file