Mac Studio M2 Ultra 128GBコスパ重視
128 GB Unified (+48)
C
This setup is broadly balanced for this model.84.5 tok/sデコード
〜$3,999 MSRP
Can Yi 1.5 9B Chat run on NVIDIA H100 80GB?
YES — Runs Great
C46Usable
Estimated from fit model
Yi 1.5 9B Chat needs ~15.7 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~126 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) — 15.7 GB, 126.0 tok/s, Runs well
15.7 GB required80.0 GB available
20% VRAM used
Fit status
Runs well
Decode
126.0 tok/s
TTFT
1537 ms
Safe context
991K
Memory
15.7 GB / 80.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsYi 1.5 9B Chat on NVIDIA H100 80GB
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: 126.0 tok/s decode · 1.5s TTFT (warm) · 315 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 | 126.0 tok/s | 838 ms | 991K |
| Coding | C | Runs well | 126.0 tok/s | 1537 ms | 991K |
| Agentic Coding | C | Runs well | 126.0 tok/s | 2235 ms | 991K |
| Reasoning | C | Runs well | 126.0 tok/s | 1816 ms | 991K |
| RAG | C | Runs well | 126.0 tok/s | 2794 ms | 991K |
Inference speed
Yi 1.5 9B Chat inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Yi 1.5 9B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 125.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 119.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 111.3 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 101.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 80.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 68.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 43.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 43.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 40.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 35.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.4 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Yi 1.5 9B Chat (9B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | D40 |
Q3_K_S | 3 | 4.4 GB | Low | D40 |
NVFP4 | 4 | 5.0 GB | Medium | D40 |
Q4_K_M | 4 | 5.5 GB | Medium | D40 |
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 | C40 |
F16Best for your GPU | 16 | 18.5 GB | Maximum | C41 |
Get started
Copy-paste commands to run Yi 1.5 9B Chat on your machine.
Run
lms load hf-bartowski--yi-1-5-9b-chat-gguf && lms server startアップグレードオプション
Yi 1.5 9B Chatを快適に動かすハードウェア
Frequently asked questions
Can NVIDIA H100 80GB run Yi 1.5 9B Chat?
Yes, NVIDIA H100 80GB can run Yi 1.5 9B Chat with a C grade (Runs well). Expected decode speed: 126.0 tok/s.
How much VRAM does Yi 1.5 9B Chat need?
Yi 1.5 9B Chat (9B parameters) requires approximately 15.7 GB of memory with Q4_K_M quantization.
What is the best quantization for Yi 1.5 9B Chat?
The recommended quantization for Yi 1.5 9B Chat is Q4_K_M, which balances quality and memory efficiency.
What speed will Yi 1.5 9B Chat run at on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Yi 1.5 9B Chat achieves approximately 126.0 tokens per second decode speed with a time-to-first-token of 1537ms using Q4_K_M quantization.
Can NVIDIA H100 80GB run Yi 1.5 9B Chat for coding?
For coding workloads, Yi 1.5 9B Chat on NVIDIA H100 80GB receives a C grade with 126.0 tok/s and 991K context.
What context window can Yi 1.5 9B Chat use on NVIDIA H100 80GB?
On NVIDIA H100 80GB, Yi 1.5 9B Chat can safely use up to 991K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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