Can Yi 34B Chat run on NVIDIA A100 80GB?

YES — Runs Great

C52Usable

Estimated from fit model

Yi 34B Chat needs ~33.3 GB VRAM. NVIDIA A100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~90 tok/s.

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

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 33.3 GB, 89.7 tok/s, Runs well

33.3 GB required80.0 GB available

42% VRAM used

Fit status

Runs well

Decode

89.7 tok/s

TTFT

2159 ms

Safe context

200K

Memory

33.3 GB / 80.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsYi 34B Chat on NVIDIA A100 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: 89.7 tok/s decode · 2.2s TTFT (warm) · 224 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	89.7 tok/s	1178 ms	200K
Coding	C	Runs well	89.7 tok/s	2159 ms	200K
Agentic Coding	C	Runs well	89.7 tok/s	3141 ms	200K
Reasoning	C	Runs well	89.7 tok/s	2552 ms	200K
RAG	C	Runs well	89.7 tok/s	3926 ms	200K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	40.7	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	31.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	31.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	29.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	23.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	20.1	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.8	Tight
RTX 4090 24GB	24 GB	Q4_K_M	12.8	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.6	Fits
RTX 3090 24GB	24 GB	Q4_K_M	11.8	Heavy offload
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	4.6	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too 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 A100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C42
Q3_K_S	3	16.7 GB	Low	C43
NVFP4	4	19.0 GB	Medium	C43
Q4_K_M	4	20.7 GB	Medium	C43
Q5_K_M	5	24.5 GB	High	C44
Q6_K	6	27.9 GB	High	C45
Q8_0Best for your GPU	8	36.4 GB	Very High	C47
F16	16	69.7 GB	Maximum	F0

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 A100 80GB run Yi 34B Chat?

Yes, NVIDIA A100 80GB can run Yi 34B Chat with a C grade (Runs well). Expected decode speed: 89.7 tok/s.

How much VRAM does Yi 34B Chat need?

Yi 34B Chat (34B parameters) requires approximately 33.3 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 A100 80GB?

On NVIDIA A100 80GB, Yi 34B Chat achieves approximately 89.7 tokens per second decode speed with a time-to-first-token of 2159ms using Q4_K_M quantization.

Can NVIDIA A100 80GB run Yi 34B Chat for coding?

For coding workloads, Yi 34B Chat on NVIDIA A100 80GB receives a C grade with 89.7 tok/s and 200K context.

What context window can Yi 34B Chat use on NVIDIA A100 80GB?

On NVIDIA A100 80GB, 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 A100 80GB See all hardware for Yi 34B Chat

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