DeepSeek Coder V2 16B needs ~15.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~83 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 with offload
Decode
82.9 tok/s
TTFT
2335 ms
Safe context
18K
Memory
15.6 GB / 16.0 GB
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 82.9 tok/s | 1274 ms | 18K |
| Coding | A | Runs with offload | 82.9 tok/s | 2335 ms | 18K |
| Agentic Coding | B | Very compromised | 44.0 tok/s | 6396 ms | 18K |
| Reasoning | A | Runs with offload | 82.9 tok/s | 2760 ms | 18K |
| RAG | B | Very compromised | 44.0 tok/s | 7996 ms | 18K |
How DeepSeek Coder V2 16B (16B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 6.2 GB | Low | A79 |
Q3_K_S | 3 | 7.8 GB | Low | A80 |
NVFP4 | 4 |
Copy-paste commands to run DeepSeek Coder V2 16B on your machine.
Run
lms load DeepSeek-Coder-V2-Lite-Instruct && lms server startYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 21B | A | 39.3 tok/s | ||
| 22B | A |
Yes, Radeon RX 7900M 16GB can run DeepSeek Coder V2 16B with a A grade (Runs with offload). Expected decode speed: 82.9 tok/s.
DeepSeek Coder V2 16B (16B parameters) requires approximately 15.6 GB of memory with Q4_K_M quantization.
The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.
On Radeon RX 7900M 16GB, DeepSeek Coder V2 16B achieves approximately 82.9 tokens per second decode speed with a time-to-first-token of 2335ms using Q4_K_M quantization.
For coding workloads, DeepSeek Coder V2 16B on Radeon RX 7900M 16GB receives a A grade with 82.9 tok/s and 18K context.
On Radeon RX 7900M 16GB, DeepSeek Coder V2 16B can safely use up to 18K tokens of context. The model's official context limit is 131K, 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-coder-v2-16b-on-rx-7900m-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A80 |
Q4_K_M | 4 | 9.8 GB | Medium | A80 |
Q5_K_MBest for your GPU | 5 | 11.5 GB | High | A79 |
Q6_K | 6 | 13.1 GB | High | F0 |
Q8_0 | 8 | 17.1 GB | Very High | F0 |
F16 | 16 | 32.8 GB | Maximum | F0 |
| 14.4 tok/s |
| 19B | A | 22.1 tok/s |
| 20B | B | 17.9 tok/s |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.