Mistral Nemo 12B needs ~12.3 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~58 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
58.3 tok/s
TTFT
3322 ms
Safe context
41K
Memory
12.3 GB / 16.0 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 | 58.3 tok/s | 1812 ms | 41K |
| Coding | B | Runs well | 58.3 tok/s | 3322 ms | 41K |
| Agentic Coding | B | Tight fit | 58.3 tok/s | 4832 ms | 41K |
| Reasoning | B | Runs well | 58.3 tok/s | 3926 ms | 41K |
| RAG | B | Tight fit | 58.3 tok/s | 6041 ms | 41K |
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 RX 9070 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B61 |
Q3_K_S | 3 | 5.9 GB | Low | B62 |
NVFP4 | 4 | 6.7 GB | Medium | B63 |
Q4_K_M | 4 | 7.3 GB | Medium | B63 |
Q5_K_M | 5 | 8.6 GB | High | B64 |
Q6_KBest for your GPU | 6 | 9.8 GB | High | B63 |
Q8_0 | 8 | 12.8 GB | Very High | F0 |
F16 | 16 | 24.6 GB | Maximum | F0 |
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoYes, RX 9070 16GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 58.3 tok/s.
Mistral Nemo 12B (12B parameters) requires approximately 12.3 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 RX 9070 16GB, Mistral Nemo 12B achieves approximately 58.3 tokens per second decode speed with a time-to-first-token of 3322ms using Q4_K_M quantization.
For coding workloads, Mistral Nemo 12B on RX 9070 16GB receives a B grade with 58.3 tok/s and 41K context.
On RX 9070 16GB, Mistral Nemo 12B can safely use up to 41K 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-rx-9070-16gb" 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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