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DeepSeek LLM 67B needs ~36.0 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q2_K quantization, expect ~8 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
18.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.0 tok/s
TTFT
64604 ms
Safe context
4K
Memory
50.8 GB / 32.0 GB
Offload
40%
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 2.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.4 tok/s | 31255 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 64604 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 117165 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76350 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 146457 ms | 4K |
How DeepSeek LLM 67B (67B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 26.1 GB | Low | F0 |
Q3_K_S | 3 | 32.8 GB | Low | F0 |
NVFP4 | 4 | 37.5 GB | Medium | F0 |
Q4_K_M | 4 | 40.9 GB | Medium | F0 |
Q5_K_M | 5 | 48.2 GB | High | F0 |
Q6_K | 6 | 54.9 GB | High | F0 |
Q8_0 | 8 | 71.7 GB | Very High | F0 |
F16 | 16 | 137.4 GB | Maximum | F0 |
Copy-paste commands to run DeepSeek LLM 67B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "deepseek-ai/deepseek-llm-67b-chat" \
--hf-file "deepseek-llm-67b-chat-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 183%.
~$3,999 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.
~$8,000 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.
~$10,000 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.
~$40,000 MSRP
