Nous Dolphin 13B needs ~25.2 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q5_K_M quantization, expect ~43 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
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.4 GB host RAM)
Decode
42.7 tok/s
TTFT
4534 ms
Safe context
14K
Memory
25.2 GB / 24.0 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 | Runs well | 61.5 tok/s | 1717 ms | 14K |
| Coding | A | Runs with offload (needs ~0.4 GB host RAM) | 42.7 tok/s | 4534 ms | 14K |
| Agentic Coding | F | Too heavy | 19.1 tok/s | 14777 ms | 14K |
| Reasoning | A | Runs with offload (needs ~0.4 GB host RAM) | 42.7 tok/s | 5359 ms | 14K |
| RAG | F | Too heavy | 19.1 tok/s | 18471 ms | 14K |
How Nous Dolphin 13B (13B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B66 |
Q3_K_S | 3 | 6.4 GB | Low | B67 |
NVFP4 | 4 | 7.3 GB | Medium | B67 |
Q4_K_M | 4 | 7.9 GB | Medium | B68 |
Q5_K_M | 5 | 9.4 GB | High | B69 |
Q6_K | 6 | 10.7 GB | High | B70 |
Q8_0Best for your GPU | 8 | 13.9 GB | Very High | A71 |
F16 | 16 | 26.7 GB | Maximum | F0 |
Copy-paste commands to run Nous Dolphin 13B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "nousresearch/Nous-Dolphin-13B" \
--hf-file "Nous-Dolphin-13B-Q5_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
 Qwen3-Coder 30B A3B Instruct | 30.5B | S | 85.4 tok/s | |
| Qwen 3.5 27B | 27B | S | 37 tok/s | |
| Qwen 3.6 27B | 27B | S | 37.1 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 88.3 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 49.1 tok/s |
Yes, RTX PRO 4000 Blackwell 24GB can run Nous Dolphin 13B with a A grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 42.7 tok/s.
Nous Dolphin 13B (13B parameters) requires approximately 25.2 GB of memory with Q5_K_M quantization.
The recommended quantization for Nous Dolphin 13B is Q5_K_M, which balances quality and memory efficiency.
On RTX PRO 4000 Blackwell 24GB, Nous Dolphin 13B achieves approximately 42.7 tokens per second decode speed with a time-to-first-token of 4534ms using Q5_K_M quantization.
For coding workloads, Nous Dolphin 13B on RTX PRO 4000 Blackwell 24GB receives a A grade with 42.7 tok/s and 14K context.
On RTX PRO 4000 Blackwell 24GB, Nous Dolphin 13B can safely use up to 14K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/nous-dolphin-13b-on-rtx-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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