willitrun·ai

Can Yi 34B Chat run on NVIDIA H200 PCIe 141GB?

YES — Runs Great

C49Usable
Estimated from fit model

Yi 34B Chat needs ~39.4 GB VRAM. NVIDIA H200 PCIe 141GB has 141.0 GB. With Q4_K_M quantization, expect ~211 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 39.4 GB, 211.1 tok/s, Runs well
39.4 GB required141.0 GB available
28% VRAM used

Fit status

Runs well

Decode

211.1 tok/s

TTFT

917 ms

Safe context

200K

Memory

39.4 GB / 141.0 GB

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime0.9 GB
Headroom14.1 GB

See how fast it feels

See how fast it feelsYi 34B Chat on NVIDIA H200 PCIe 141GB
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: 211.1 tok/s decode · 917ms TTFT (warm) · 528 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well211.1 tok/s500 ms200K
CodingCRuns well211.1 tok/s917 ms200K
Agentic CodingCRuns well211.1 tok/s1334 ms200K
ReasoningCRuns well211.1 tok/s1084 ms200K
RAGCRuns well211.1 tok/s1668 ms200K

Inference speed

Yi 34B Chat inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 34B Chat at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~41 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 / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M40.7Tight
MacBook Pro M4 Max 128GB
128 GBQ4_K_M31.4Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M31.4Fits
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M29.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M24.3Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M23.0Fits
RX 7900 XTX 24GB
24 GBQ4_K_M20.1Heavy offload
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M19.8Tight
NVIDIARTX 4090 24GB
24 GBQ4_K_M12.8Heavy offload
MacBook Pro M3 Max 64GB
64 GBQ4_K_M12.6Fits
NVIDIARTX 3090 24GB
24 GBQ4_K_M11.8Heavy offload
MacBook Pro M1 Max 64GB
64 GBQ4_K_M11.5Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M4.6Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big

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 34B Chat (34B params) fits at each quantization level on NVIDIA H200 PCIe 141GB (141.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowD40
Q3_K_S
3
16.7 GB
LowD40
NVFP4
4
19.0 GB
MediumC40
Q4_K_M
4
20.7 GB
MediumC40
Q5_K_M
5
24.5 GB
HighC41
Q6_K
6
27.9 GB
HighC41
Q8_0
8
36.4 GB
Very HighC42
F16Best for your GPU
16
69.7 GB
MaximumC48

Get started

Copy-paste commands to run Yi 34B Chat on your machine.

Run

lms load Yi-34B-Chat && lms server start

Frequently asked questions

Can NVIDIA H200 PCIe 141GB run Yi 34B Chat?

Yes, NVIDIA H200 PCIe 141GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 211.1 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 39.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 34B Chat?

The recommended quantization for Yi 34B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 34B Chat run at on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Yi 34B Chat achieves approximately 211.1 tokens per second decode speed with a time-to-first-token of 917ms using Q4_K_M quantization.

Can NVIDIA H200 PCIe 141GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on NVIDIA H200 PCIe 141GB receives a C grade with 211.1 tok/s and 200K context.

What context window can Yi 34B Chat use on NVIDIA H200 PCIe 141GB?

On NVIDIA H200 PCIe 141GB, Yi 34B Chat can safely use up to 200K tokens of context. The model's official context limit is 200K, but available memory constrains the safe maximum.

See all results for NVIDIA H200 PCIe 141GBSee all hardware for Yi 34B Chat
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