Nous Dolphin 13B needs ~25.2 GB VRAM. NVIDIA A10 24GB has 24.0 GB. With Q5_K_M quantization, expect ~35 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
34.6 tok/s
TTFT
5593 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 | 51.0 tok/s | 2071 ms | 14K |
| Coding | A | Runs with offload (needs ~0.4 GB host RAM) | 34.6 tok/s | 5593 ms | 14K |
| Agentic Coding | F | Too heavy | 15.1 tok/s | 18703 ms | 14K |
| Reasoning | A | Runs with offload (needs ~0.4 GB host RAM) | 34.6 tok/s | 6610 ms | 14K |
| RAG | F | Too heavy | 15.1 tok/s | 23379 ms | 14K |
Inference speed
Estimated decode speed (tokens/sec) for Nous Dolphin 13B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q5_K_M | 130.8 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q5_K_M | 60.7 | Fits |
| RTX 4090 24GB | 24 GB | Q5_K_M | 56.7 | Offloads |
RX 7900 XTX 24GB | 24 GB | Q5_K_M | 51.1 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q5_K_M | 50.6 | Fits |
| RTX 3090 24GB | 24 GB | Q5_K_M | 48.5 | Offloads |
Mac Studio M1 Ultra 128GB | 128 GB | Q5_K_M | 47.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q5_K_M | 33.0 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q5_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q5_K_M | 24.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q5_K_M | 20.6 | Too big |
MacBook Pro M4 Pro 48GB | 48 GB | Q5_K_M | 20.2 | Fits |
| RTX 4070 12GB | 12 GB | Q5_K_M | 7.2 | Too big |
| RTX 3060 12GB | 12 GB | Q5_K_M | 4.5 | Too big |
| RTX 4060 8GB | 8 GB | Q5_K_M | 3.2 | Too big |
Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Nous Dolphin 13B (13B params) fits at each quantization level on NVIDIA A10 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 | 70.8 tok/s | |
| Qwen 3.5 27B | 27B | S | 30.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 30.8 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 73.2 tok/s | |
| Qwen 3.5 35B A3B | 35B | A | 39.6 tok/s |
Yes, NVIDIA A10 24GB can run Nous Dolphin 13B with a A grade (Runs with offload (needs ~0.4 GB host RAM)). Expected decode speed: 34.6 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 NVIDIA A10 24GB, Nous Dolphin 13B achieves approximately 34.6 tokens per second decode speed with a time-to-first-token of 5593ms using Q5_K_M quantization.
For coding workloads, Nous Dolphin 13B on NVIDIA A10 24GB receives a A grade with 34.6 tok/s and 14K context.
On NVIDIA A10 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-a10-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: