MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)546 GB/s (+34)
B
This setup is broadly balanced for this model.44.5 tok/sデコード
〜$2,499 MSRP
Can Mistral Nemo 12B run on Radeon Pro W6800 32GB?
YES — Runs Great
B61Good
Estimated from fit model
Mistral Nemo 12B needs ~13.9 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 13.9 GB, 42.1 tok/s, Runs well
13.9 GB required32.0 GB available
43% VRAM used
Fit status
Runs well
Decode
42.1 tok/s
TTFT
4598 ms
Safe context
128K
Memory
13.9 GB / 32.0 GB
Memory breakdown
Weights7.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsMistral Nemo 12B on Radeon Pro W6800 32GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 42.1 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 42.1 tok/s | 2508 ms | 128K |
| Coding | B | Runs well | 42.1 tok/s | 4598 ms | 128K |
| Agentic Coding | B | Runs well | 42.1 tok/s | 6688 ms | 128K |
| Reasoning | B | Runs well | 42.1 tok/s | 5434 ms | 128K |
| RAG | B | Runs well | 42.1 tok/s | 8360 ms | 128K |
Inference speed
Mistral Nemo 12B inference speed — tokens per second by GPU & Mac
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.
Quantization options
How Mistral Nemo 12B (12B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 4.7 GB | Low | B56 |
Q3_K_S | 3 | 5.9 GB | Low | B56 |
NVFP4 | 4 | 6.7 GB | Medium | B57 |
Q4_K_M | 4 | 7.3 GB | Medium | B57 |
Q5_K_M | 5 | 8.6 GB | High | B57 |
Q6_K | 6 | 9.8 GB | High | B58 |
Q8_0 | 8 | 12.8 GB | Very High | B59 |
F16Best for your GPU | 16 | 24.6 GB | Maximum | B61 |
Get started
Copy-paste commands to run Mistral Nemo 12B on your machine.
Run
ollama run mistral-nemoアップグレードオプション
Mistral Nemo 12Bを快適に動かすハードウェア
Frequently asked questions
Can Radeon Pro W6800 32GB run Mistral Nemo 12B?
Yes, Radeon Pro W6800 32GB can run Mistral Nemo 12B with a B grade (Runs well). Expected decode speed: 42.1 tok/s.
How much VRAM does Mistral Nemo 12B need?
Mistral Nemo 12B (12B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Mistral Nemo 12B?
The recommended quantization for Mistral Nemo 12B is Q4_K_M, which balances quality and memory efficiency.
What speed will Mistral Nemo 12B run at on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, Mistral Nemo 12B achieves approximately 42.1 tokens per second decode speed with a time-to-first-token of 4598ms using Q4_K_M quantization.
Can Radeon Pro W6800 32GB run Mistral Nemo 12B for coding?
For coding workloads, Mistral Nemo 12B on Radeon Pro W6800 32GB receives a B grade with 42.1 tok/s and 128K context.
What context window can Mistral Nemo 12B use on Radeon Pro W6800 32GB?
On Radeon Pro W6800 32GB, 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.
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