DeepSeek LLM 7B needs ~13.3 GB but Radeon PRO W7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
5.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
10.2 tok/s
TTFT
18894 ms
Safe context
4K
Memory
13.3 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.3 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 20.2 tok/s | 5230 ms | 4K |
| Coding | F | Too heavy | 10.2 tok/s | 18894 ms | 4K |
| Agentic Coding | F | Too heavy | 6.0 tok/s | 47176 ms | 4K |
| Reasoning | F | Too heavy | 10.2 tok/s | 22330 ms | 4K |
| RAG | F | Too heavy | 6.0 tok/s | 58970 ms | 4K |
How DeepSeek LLM 7B (7B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C52 |
Q3_K_S | 3 | 3.4 GB | Low | C52 |
NVFP4 | 4 | 3.9 GB | Medium | C52 |
Q4_K_M | 4 | 4.3 GB | Medium | C52 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C51 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
アップグレードオプション
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$329 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$349 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 238%.
〜$449 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$899 MSRP
No, DeepSeek LLM 7B requires more memory than Radeon PRO W7600 8GB provides.
DeepSeek LLM 7B (7B parameters) requires approximately 13.3 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek LLM 7B is Q4_K_M, which balances quality and memory efficiency.
On Radeon PRO W7600 8GB, DeepSeek LLM 7B achieves approximately 10.2 tokens per second decode speed with a time-to-first-token of 18894ms using Q4_K_M quantization.
For coding workloads, DeepSeek LLM 7B on Radeon PRO W7600 8GB receives a F grade with 10.2 tok/s and 4K context.
On Radeon PRO W7600 8GB, DeepSeek LLM 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/deepseek-llm-7b-on-radeon-pro-w7600-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
