Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+76)
C
This setup is broadly balanced for this model.21 tok/sデコード
〜$6,999 MSRP
Can Qwen2.5 1.5B Instruct run on NVIDIA B200 180GB?
YES — Runs Great
D40Poor
Estimated from fit model
Qwen2.5 1.5B Instruct needs ~20.3 GB VRAM. NVIDIA B200 180GB has 180.0 GB. With Q4_K_M quantization, expect ~21 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) — 20.3 GB, 21.0 tok/s, Runs well
20.3 GB required180.0 GB available
11% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
14.6M
Memory
20.3 GB / 180.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom18.0 GB
See how fast it feels
See how fast it feelsQwen2.5 1.5B Instruct on NVIDIA B200 180GB
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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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 | D | Runs well | 21.0 tok/s | 5029 ms | 12.8M |
| Coding | D | Runs well | 21.0 tok/s | 9219 ms | 14.6M |
| Agentic Coding | D | Runs well | 21.0 tok/s | 13410 ms | 14.6M |
| Reasoning | D | Runs well | 21.0 tok/s | 10895 ms | 14.6M |
| RAG | D | Runs well | 21.0 tok/s | 16762 ms | 14.6M |
Quantization options
How Qwen2.5 1.5B Instruct (1.5B params) fits at each quantization level on NVIDIA B200 180GB (180.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D37 |
Q3_K_S | 3 | 0.7 GB | Low | D37 |
NVFP4 | 4 | 0.8 GB | Medium | D37 |
Q4_K_M | 4 | 0.9 GB | Medium | D37 |
Q5_K_M | 5 | 1.1 GB | High | D37 |
Q6_K | 6 | 1.2 GB | High | D37 |
Q8_0 | 8 | 1.6 GB | Very High | D37 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | D37 |
Get started
Copy-paste commands to run Qwen2.5 1.5B Instruct on your machine.
Run
lms load hf-qwen--qwen2-5-1-5b-instruct-gguf && lms server startアップグレードオプション
Qwen2.5 1.5B Instructを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA B200 180GB run Qwen2.5 1.5B Instruct?
Yes, NVIDIA B200 180GB can run Qwen2.5 1.5B Instruct with a D grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does Qwen2.5 1.5B Instruct need?
Qwen2.5 1.5B Instruct (1.5B parameters) requires approximately 20.3 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen2.5 1.5B Instruct?
The recommended quantization for Qwen2.5 1.5B Instruct is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen2.5 1.5B Instruct run at on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Qwen2.5 1.5B Instruct achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can NVIDIA B200 180GB run Qwen2.5 1.5B Instruct for coding?
For coding workloads, Qwen2.5 1.5B Instruct on NVIDIA B200 180GB receives a D grade with 21.0 tok/s and 14.6M context.
What context window can Qwen2.5 1.5B Instruct use on NVIDIA B200 180GB?
On NVIDIA B200 180GB, Qwen2.5 1.5B Instruct can safely use up to 14.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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