Mac Studio M3 Ultra 256GBGünstige Wahl
256 GB Unified (+160)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $6,999 MSRP
Can Qwen3 8B DeepSeek v3.2 Speciale Distill run on NVIDIA GH200 96GB?
YES — Runs Great
C46Usable
Estimated from fit model
Qwen3 8B DeepSeek v3.2 Speciale Distill needs ~16.6 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~112 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 16.6 GB, 112.0 tok/s, Runs well
16.6 GB required96.0 GB available
17% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
1.4M
Memory
16.6 GB / 96.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom9.6 GB
See how fast it feels
See how fast it feelsQwen3 8B DeepSeek v3.2 Speciale Distill on NVIDIA GH200 96GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 112.0 tok/s | 943 ms | 1.4M |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 1.4M |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 1.4M |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 1.4M |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 1.4M |
Quantization options
How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D39 |
Q3_K_S | 3 | 3.9 GB | Low | D39 |
NVFP4 | 4 | 4.5 GB | Medium | D39 |
Q4_K_M | 4 | 4.9 GB | Medium | D39 |
Q5_K_M | 5 | 5.8 GB | High | D39 |
Q6_K | 6 | 6.6 GB | High | D39 |
Q8_0 | 8 | 8.6 GB | Very High | D39 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C40 |
Get started
Copy-paste commands to run Qwen3 8B DeepSeek v3.2 Speciale Distill on your machine.
Run
lms load hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf && lms server startUpgrade-Optionen
Hardware, die Qwen3 8B DeepSeek v3.2 Speciale Distill gut ausführt
Frequently asked questions
Can NVIDIA GH200 96GB run Qwen3 8B DeepSeek v3.2 Speciale Distill?
Yes, NVIDIA GH200 96GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does Qwen3 8B DeepSeek v3.2 Speciale Distill need?
Qwen3 8B DeepSeek v3.2 Speciale Distill (8B parameters) requires approximately 16.6 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill?
The recommended quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3 8B DeepSeek v3.2 Speciale Distill run at on NVIDIA GH200 96GB?
On NVIDIA GH200 96GB, Qwen3 8B DeepSeek v3.2 Speciale Distill achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can NVIDIA GH200 96GB run Qwen3 8B DeepSeek v3.2 Speciale Distill for coding?
For coding workloads, Qwen3 8B DeepSeek v3.2 Speciale Distill on NVIDIA GH200 96GB receives a C grade with 112.0 tok/s and 1.4M context.
What context window can Qwen3 8B DeepSeek v3.2 Speciale Distill use on NVIDIA GH200 96GB?
On NVIDIA GH200 96GB, Qwen3 8B DeepSeek v3.2 Speciale Distill can safely use up to 1.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf-on-gh200-96gb" 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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