RX 7900 XT 20GB经济之选
20 GB VRAM (+4)800 GB/s (+512)
B
Raises estimated decode speed by about 187%.105.7 tok/s 解码
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can Ministral 8B run on RX 7600 XT 16GB?
YES — Runs Great
B61Good
Estimated from fit model
Ministral 8B needs ~9.6 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 9.6 GB, 36.8 tok/s, Runs well
9.6 GB required16.0 GB available
60% VRAM used
Fit status
Runs well
Decode
36.8 tok/s
TTFT
5261 ms
Safe context
63K
Memory
9.6 GB / 16.0 GB
Memory breakdown
Weights4.9 GB
KV Cache2.2 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsMinistral 8B on RX 7600 XT 16GB
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: 36.8 tok/s decode · 5.3s TTFT (warm) · 92 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 | 36.8 tok/s | 2870 ms | 63K |
| Coding | B | Runs well | 36.8 tok/s | 5261 ms | 63K |
| Agentic Coding | B | Runs well | 36.8 tok/s | 7653 ms | 63K |
| Reasoning | B | Runs well | 36.8 tok/s | 6218 ms | 63K |
| RAG | B | Runs well | 36.8 tok/s | 9566 ms | 63K |
Inference speed
Ministral 8B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Ministral 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 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 |
| RTX 4070 12GB | 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 |
| RTX 3060 12GB | 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 |
| RTX 4060 8GB | 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.
Quantization options
How Ministral 8B (8B params) fits at each quantization level on RX 7600 XT 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B57 |
Q3_K_S | 3 | 3.9 GB | Low | B57 |
NVFP4 | 4 | 4.5 GB | Medium | B58 |
Q4_K_M | 4 | 4.9 GB | Medium | B58 |
Q5_K_M | 5 | 5.8 GB | High | B59 |
Q6_K | 6 | 6.6 GB | High | B60 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | B61 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministral升级选项
能流畅运行 Ministral 8B 的硬件
Frequently asked questions
Can RX 7600 XT 16GB run Ministral 8B?
Yes, RX 7600 XT 16GB can run Ministral 8B with a B grade (Runs well). Expected decode speed: 36.8 tok/s.
How much VRAM does Ministral 8B need?
Ministral 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Ministral 8B?
The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.
What speed will Ministral 8B run at on RX 7600 XT 16GB?
On RX 7600 XT 16GB, Ministral 8B achieves approximately 36.8 tokens per second decode speed with a time-to-first-token of 5261ms using Q4_K_M quantization.
Can RX 7600 XT 16GB run Ministral 8B for coding?
For coding workloads, Ministral 8B on RX 7600 XT 16GB receives a B grade with 36.8 tok/s and 63K context.
What context window can Ministral 8B use on RX 7600 XT 16GB?
On RX 7600 XT 16GB, Ministral 8B can safely use up to 63K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-8b-on-rx-7600-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: