DeepSeek R1 Distill 8B needs ~8.5 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 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
0.5 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.3 GB host RAM)
Decode
19.1 tok/s
TTFT
10156 ms
Safe context
12K
Memory
8.5 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Tight fit | 29.1 tok/s | 3627 ms | 12K |
| Coding | B | Runs with offload | 17.7 tok/s | 10918 ms | 12K |
| Agentic Coding | F | Too heavy | 12.4 tok/s | 22797 ms | 12K |
| Reasoning | B | Runs with offload (needs ~0.3 GB host RAM) | 19.1 tok/s | 12003 ms | 12K |
| RAG | F | Too heavy | 12.4 tok/s | 28497 ms | 12K |
How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A70 |
Q3_K_S | 3 | 3.9 GB | Low | B70 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.
Run
ollama run deepseek-r1:8bUpgrade options
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 93%.
~$329 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 132%.
~$349 MSRP
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 199%.
~$449 MSRP
Yes, Radeon Pro W7500 8GB can run DeepSeek R1 Distill 8B with a B grade (Runs with offload). Expected decode speed: 17.7 tok/s.
DeepSeek R1 Distill 8B (8B parameters) requires approximately 8.5 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek R1 Distill 8B is Q4_K_M, which balances quality and memory efficiency.
On Radeon Pro W7500 8GB, DeepSeek R1 Distill 8B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10918ms using Q4_K_M quantization.
For coding workloads, DeepSeek R1 Distill 8B on Radeon Pro W7500 8GB receives a B grade with 17.7 tok/s and 12K context.
On Radeon Pro W7500 8GB, DeepSeek R1 Distill 8B can safely use up to 12K tokens of context. The model's official context limit is 33K, 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/deepseek-r1-distill-8b-on-radeon-pro-w7500-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| B70 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | B69 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.