Qwen 3 8B needs ~10.7 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~43 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
43.0 tok/s
TTFT
4507 ms
Safe context
113K
Memory
10.7 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 43.0 tok/s | 2458 ms | 113K |
| Coding | S | Runs well | 43.0 tok/s | 4507 ms | 113K |
| Agentic Coding | S | Runs well | 43.0 tok/s | 6556 ms | 113K |
| Reasoning | S | Runs well | 43.0 tok/s | 5326 ms | 113K |
| RAG | S | Runs well | 43.0 tok/s | 8194 ms | 113K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen 3 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| 32 GB | Q4_K_M | 112.0 | Fits | |
| 24 GB | Q4_K_M | 112.0 | Fits | |
| 16 GB | Q4_K_M | 112.0 | Fits | |
| 24 GB | Q4_K_M | 112.0 | Fits | |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 102.2 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 96.9 | Fits |
| 12 GB | Q4_K_M | 83.3 | Fits | |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 82.6 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 52.9 | Fits |
| 12 GB | Q4_K_M | 52.3 | Fits | |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 48.5 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 42.6 | Fits |
| 8 GB | Q4_K_M | 25.1 | Heavy offload |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen 3 8B (8B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A84 |
Q3_K_S | 3 | 3.9 GB | Low | A85 |
NVFP4 | 4 | 4.5 GB | Medium | S85 |
Q4_K_M | 4 | 4.9 GB | Medium | S85 |
Q5_K_M | 5 | 5.8 GB | High | S86 |
Q6_K | 6 | 6.6 GB | High | S86 |
Q8_0 | 8 | 8.6 GB | Very High | S88 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | S90 |
Copy-paste commands to run Qwen 3 8B on your machine.
Run
ollama run qwen3:8bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 30.5B | S | 29.5 tok/s | ||
| 27B | S | 12.8 tok/s | ||
| 27B | S | 12.8 tok/s | ||
| 30B | S | 30.5 tok/s | ||
| 9B | S | 38.2 tok/s |
Yes, NVIDIA L4 24GB can run Qwen 3 8B with a S grade (Runs well). Expected decode speed: 43.0 tok/s.
Qwen 3 8B (8B parameters) requires approximately 10.7 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3 8B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA L4 24GB, Qwen 3 8B achieves approximately 43.0 tokens per second decode speed with a time-to-first-token of 4507ms using Q4_K_M quantization.
For coding workloads, Qwen 3 8B on NVIDIA L4 24GB receives a S grade with 43.0 tok/s and 113K context.
On NVIDIA L4 24GB, Qwen 3 8B can safely use up to 113K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3-8b-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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