MacBook Pro M3 Max 128GBコスパ重視
128 GB Unified (+48)
C
This setup is broadly balanced for this model.21 tok/sデコード
〜$2,499 MSRP
Can DeepSeek R1 Distill Qwen 1.5B run on NVIDIA H800 80GB?
YES — Runs Great
C40Usable
Estimated from fit model
DeepSeek R1 Distill Qwen 1.5B needs ~10.3 GB VRAM. NVIDIA H800 80GB has 80.0 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) — 10.3 GB, 21.0 tok/s, Runs well
10.3 GB required80.0 GB available
13% VRAM used
Fit status
Runs well
Decode
21.0 tok/s
TTFT
9219 ms
Safe context
6.4M
Memory
10.3 GB / 80.0 GB
Memory breakdown
Weights0.9 GB
KV Cache0.2 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsDeepSeek R1 Distill Qwen 1.5B on NVIDIA H800 80GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.
Given information:
- Speed = 60 mph (miles per hour)
- Time = 2.5 hours
Formula: Distance = Speed x Time
Calculation:
Distance = 60 mph x 2.5 h
= 150 miles
The train travels 150 miles.
Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.
Breakdown:
- First half: 1.25 hours at 60 mph = 75 miles
- Stop: 20 minutes (no distance)
- Second half: 1.25 hours at 60 mph = 75 miles
Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph
The distance stays the same but average speed drops to 53 mph because of the stop.
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 | C | Runs well | 21.0 tok/s | 5029 ms | 5.6M |
| Coding | C | Runs well | 21.0 tok/s | 9219 ms | 6.4M |
| Agentic Coding | C | Runs well | 21.0 tok/s | 13410 ms | 6.4M |
| Reasoning | C | Runs well | 21.0 tok/s | 10895 ms | 6.4M |
| RAG | C | Runs well | 21.0 tok/s | 16762 ms | 6.4M |
Quantization options
How DeepSeek R1 Distill Qwen 1.5B (1.5B params) fits at each quantization level on NVIDIA H800 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.6 GB | Low | D40 |
Q3_K_S | 3 | 0.7 GB | Low | D40 |
NVFP4 | 4 | 0.8 GB | Medium | D40 |
Q4_K_M | 4 | 0.9 GB | Medium | D40 |
Q5_K_M | 5 | 1.1 GB | High | D40 |
Q6_K | 6 | 1.2 GB | High | D40 |
Q8_0 | 8 | 1.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 3.1 GB | Maximum | D40 |
Get started
Copy-paste commands to run DeepSeek R1 Distill Qwen 1.5B on your machine.
Run
lms load hf-unsloth--deepseek-r1-distill-qwen-1-5b-gguf && lms server startアップグレードオプション
DeepSeek R1 Distill Qwen 1.5Bを快適に動かすハードウェア
Mac Studio M2 Ultra 128GBコスパ最良
128 GB Unified (+48)
C
This setup is broadly balanced for this model.21 tok/sデコード
〜$3,999 MSRP
NVIDIA DGX Spark 128GBNVIDIAアップグレード128 GB Unified (+48)
C
Adds memory headroom for longer context windows and future model growth.21 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
Frequently asked questions
Can NVIDIA H800 80GB run DeepSeek R1 Distill Qwen 1.5B?
Yes, NVIDIA H800 80GB can run DeepSeek R1 Distill Qwen 1.5B with a C grade (Runs well). Expected decode speed: 21.0 tok/s.
How much VRAM does DeepSeek R1 Distill Qwen 1.5B need?
DeepSeek R1 Distill Qwen 1.5B (1.5B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
What is the best quantization for DeepSeek R1 Distill Qwen 1.5B?
The recommended quantization for DeepSeek R1 Distill Qwen 1.5B is Q4_K_M, which balances quality and memory efficiency.
What speed will DeepSeek R1 Distill Qwen 1.5B run at on NVIDIA H800 80GB?
On NVIDIA H800 80GB, DeepSeek R1 Distill Qwen 1.5B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.
Can NVIDIA H800 80GB run DeepSeek R1 Distill Qwen 1.5B for coding?
For coding workloads, DeepSeek R1 Distill Qwen 1.5B on NVIDIA H800 80GB receives a C grade with 21.0 tok/s and 6.4M context.
What context window can DeepSeek R1 Distill Qwen 1.5B use on NVIDIA H800 80GB?
On NVIDIA H800 80GB, DeepSeek R1 Distill Qwen 1.5B can safely use up to 6.4M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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