MacBook Pro M4 Max 48GBコスパ重視
48 GB Unified (+16)
C
This setup is broadly balanced for this model.68.3 tok/sデコード
〜$2,499 MSRP
Can Qwen3.5 9B run on RTX 5000 Ada 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
Qwen3.5 9B needs ~10.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~84 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 10.9 GB, 83.9 tok/s, Runs well
10.9 GB required32.0 GB available
34% VRAM used
Fit status
Runs well
Decode
83.9 tok/s
TTFT
2307 ms
Safe context
335K
Memory
10.9 GB / 32.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsQwen3.5 9B on RTX 5000 Ada 32GB
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: 83.9 tok/s decode · 2.3s TTFT (warm) · 210 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 | 83.9 tok/s | 1258 ms | 335K |
| Coding | C | Runs well | 83.9 tok/s | 2307 ms | 335K |
| Agentic Coding | C | Runs well | 83.9 tok/s | 3355 ms | 335K |
| Reasoning | C | Runs well | 83.9 tok/s | 2726 ms | 335K |
| RAG | C | Runs well | 83.9 tok/s | 4194 ms | 335K |
Quantization options
How Qwen3.5 9B (9B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C44 |
Q3_K_S | 3 | 4.4 GB | Low | C44 |
NVFP4 | 4 | 5.0 GB | Medium | C44 |
Q4_K_M | 4 | 5.5 GB | Medium | C44 |
Q5_K_M | 5 | 6.5 GB | High | C45 |
Q6_K | 6 | 7.4 GB | High | C45 |
Q8_0 | 8 | 9.6 GB | Very High | C46 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C50 |
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 "unsloth/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
Frequently asked questions
Can RTX 5000 Ada 32GB run Qwen3.5 9B?
Yes, RTX 5000 Ada 32GB can run Qwen3.5 9B with a C grade (Runs well). Expected decode speed: 83.9 tok/s.
How much VRAM does Qwen3.5 9B need?
Qwen3.5 9B (9B parameters) requires approximately 10.9 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 9B?
The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 9B run at on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Qwen3.5 9B achieves approximately 83.9 tokens per second decode speed with a time-to-first-token of 2307ms using Q4_K_M quantization.
Can RTX 5000 Ada 32GB run Qwen3.5 9B for coding?
For coding workloads, Qwen3.5 9B on RTX 5000 Ada 32GB receives a C grade with 83.9 tok/s and 335K context.
What context window can Qwen3.5 9B use on RTX 5000 Ada 32GB?
On RTX 5000 Ada 32GB, Qwen3.5 9B can safely use up to 335K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-9b-gguf-on-rtx-5000-ada-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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