Nous Dolphin 13B needs ~26.0 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q5_K_M quantization, expect ~131 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 well
Decode
130.8 tok/s
TTFT
1480 ms
Safe context
16K
Memory
26.0 GB / 32.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 130.8 tok/s | 807 ms | 16K |
| Coding | A | Runs well | 130.8 tok/s | 1480 ms | 16K |
| Agentic Coding | B | Very compromised (needs ~1.5 GB host RAM) | 69.8 tok/s | 4034 ms | 16K |
| Reasoning | A | Runs well | 130.8 tok/s | 1749 ms | 16K |
| RAG | B | Very compromised (needs ~1.5 GB host RAM) | 69.8 tok/s | 5042 ms | 16K |
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 RTX 5090 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.1 GB | Low | B64 |
Q3_K_S | 3 | 6.4 GB | Low | B65 |
NVFP4 | 4 | 7.3 GB | Medium | B65 |
Q4_K_M | 4 | 7.9 GB | Medium | B65 |
Q5_K_M | 5 | 9.4 GB | High | B66 |
Q6_K | 6 | 10.7 GB | High | B67 |
Q8_0 | 8 | 13.9 GB | Very High | B68 |
F16Best for your GPU | 16 | 26.7 GB | Maximum | B69 |
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 | 181.6 tok/s | |
| Qwen 3.5 27B | 27B | S | 78.7 tok/s | |
| Qwen 3.6 27B | 27B | S | 79 tok/s | |
| Qwen 3.6 35B A3B | 35B | S | 128.2 tok/s | |
| Qwen3-VL 30B A3B Instruct | 30B | S | 187.8 tok/s |
Yes, RTX 5090 32GB can run Nous Dolphin 13B with a A grade (Runs well). Expected decode speed: 130.8 tok/s.
Nous Dolphin 13B (13B parameters) requires approximately 26.0 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 5090 32GB, Nous Dolphin 13B achieves approximately 130.8 tokens per second decode speed with a time-to-first-token of 1480ms using Q5_K_M quantization.
For coding workloads, Nous Dolphin 13B on RTX 5090 32GB receives a A grade with 130.8 tok/s and 16K context.
On RTX 5090 32GB, Nous Dolphin 13B can safely use up to 16K tokens of context. The model's official context limit is 16K, 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/nous-dolphin-13b-on-rtx-5090-32gb" 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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