Mistral Nemo 12B needs ~15.8 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~80 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
79.7 tok/s
TTFT
2428 ms
Safe context
128K
Memory
15.8 GB / 48.0 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 | B | Runs well | 79.7 tok/s | 1325 ms | 128K |
| Coding | B | Runs well | 79.7 tok/s | 2428 ms | 128K |
| Agentic Coding | B | Runs well | 79.7 tok/s | 3532 ms | 128K |
| Reasoning | B | Runs well | 79.7 tok/s | 2870 ms | 128K |
| RAG | B | Runs well | 79.7 tok/s | 4415 ms | 128K |
Inference speed
Estimated decode speed (tokens/sec) for Mistral Nemo 12B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~168 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 168.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.5 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 101.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 96.2 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 94.2 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 81.8 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 68.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 64.6 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 58.3 | Offloads |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 44.5 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 44.5 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 35.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 34.2 | Offloads |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 32.3 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 27.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 9.7 | Too big |
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 Mistral Nemo 12B (12B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | C54 |
Q3_K_S | 3 | 5.9 GB | Low | C54 |
NVFP4 | 4 | 6.7 GB | Medium | C54 |
Q4_K_M | 4 | 7.3 GB | Medium | C54 |
Q5_K_M | 5 | 8.6 GB | High | C55 |
Q6_K | 6 | 9.8 GB | High | B55 |
Q8_0 | 8 | 12.8 GB | Very High | B56 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | B60 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoYes, NVIDIA A40 48GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 79.7 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 15.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A40 48GB, Mistral Nemo 12B achieves approximately 79.7 tokens per second decode speed with a time-to-first-token of 2428ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on NVIDIA A40 48GB receives a B grade with 79.7 tok/s and 128K context.
On NVIDIA A40 48GB, Mistral Nemo 12B can safely use up to 128K tokens of context. The model's official context limit is 128K, 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/mistral-nemo-12b-on-a40-48gb" 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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