NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 323%.126 tok/sデコード
Raises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can Qwen3.5 9B run on NVIDIA DGX Spark 128GB?
YES — With F16
C42Usable
Estimated from fit model
Qwen3.5 9B needs ~33.8 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~12 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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.
Qwen3.5 9B at Q4_K_M needs 7.7 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (33.8 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 20.8 GB, 29.8 tok/s, Runs well
20.8 GB required108.8 GB available
19% VRAM used
Fit status
Runs well
Decode
29.8 tok/s
TTFT
6489 ms
Safe context
1.4M
Memory
20.8 GB / 108.8 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsQwen3.5 9B on NVIDIA DGX Spark 128GB
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: 29.8 tok/s decode · 6.5s TTFT (warm) · 75 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 29.8 tok/s | 3539 ms | 1.4M |
| Coding | F | Too heavy | 5.4 tok/s | 36049 ms | 4K |
| Agentic Coding | C | Runs well | 29.8 tok/s | 9438 ms | 1.4M |
| Reasoning | C | Runs well | 29.8 tok/s | 7669 ms | 1.4M |
| RAG | C | Runs well | 29.8 tok/s | 11798 ms | 1.4M |
Quantization options
How Qwen3.5 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D39 |
Q3_K_S | 3 | 4.4 GB | Low | D39 |
NVFP4 | 4 | 5.0 GB | Medium | D39 |
Q4_K_M | 4 | 5.5 GB | Medium | D39 |
Q5_K_M | 5 | 6.5 GB | High | D40 |
Q6_K | 6 | 7.4 GB | High | D40 |
Q8_0 | 8 | 9.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C41 |
Get started
Copy-paste commands to run Qwen3.5 9B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "lmstudio-community/Qwen3.5-9B-GGUF" \
--hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99アップグレードオプション
Qwen3.5 9Bを快適に動かすハードウェア
NVIDIA H200 141GBコスパ重視NVIDIA H200 PCIe 141GBコスパ最良141 GB VRAM (+13)4800 GB/s (+4527)
C
Raises estimated decode speed by about 323%.126 tok/sデコード
Raises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレード180 GB VRAM (+52)8000 GB/s (+7727)
C
Raises estimated decode speed by about 323%.126 tok/sデコード
Raises estimated decode speed by about 323%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run Qwen3.5 9B?
Yes, NVIDIA DGX Spark 128GB can run Qwen3.5 9B at F16 quantization (Runs well). The recommended Q4_K_M requires 7.7 GB which exceeds available memory, but at F16 it needs only 33.8 GB. Expected decode speed: 12.4 tok/s.
How much VRAM does Qwen3.5 9B need?
Qwen3.5 9B (9B parameters) requires approximately 7.7 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 33.8 GB.
What is the best quantization for Qwen3.5 9B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 33.8 GB.
What speed will Qwen3.5 9B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Qwen3.5 9B achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15576ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run Qwen3.5 9B for coding?
For coding workloads, Qwen3.5 9B on NVIDIA DGX Spark 128GB receives a F grade with 5.4 tok/s and 4K context.
What context window can Qwen3.5 9B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Qwen3.5 9B can safely use up to 1.2M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Qwen3.5 9B?
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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