Can Yi 1.5 34B run on NVIDIA V100 32GB?

YES — Tight Fit

B63Good

Estimated from fit model

Yi 1.5 34B needs ~28.8 GB VRAM. NVIDIA V100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~32 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.8 GB, 31.6 tok/s, Tight fit

28.8 GB required32.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

31.6 tok/s

TTFT

6133 ms

Safe context

Memory

28.8 GB / 32.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on NVIDIA V100 32GB
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: 31.6 tok/s decode · 6.1s TTFT (warm) · 79 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	B	Tight fit	31.6 tok/s	3345 ms	4K
Coding	B	Tight fit	31.6 tok/s	6133 ms	4K
Agentic Coding	B	Runs with offload (needs ~0.3 GB host RAM)	26.3 tok/s	10711 ms	4K
Reasoning	B	Tight fit	31.6 tok/s	7248 ms	4K
RAG	B	Runs with offload (needs ~0.3 GB host RAM)	26.3 tok/s	13388 ms	4K

Inference speed

Yi 1.5 34B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 1.5 34B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~63 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	62.9	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
RTX 4090 24GB	24 GB	Q4_K_M	21.7	Heavy offload
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 3090 24GB	24 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.8	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.0	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 1.5 34B (34B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B61
Q3_K_S	3	16.7 GB	Low	B62
NVFP4	4	19.0 GB	Medium	B62
Q4_K_M	4	20.7 GB	Medium	B62
Q5_K_MBest for your GPU	5	24.5 GB	High	B61
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Get started

Copy-paste commands to run Yi 1.5 34B on your machine.

Run

lms load Yi-1.5-34B-Chat && lms server start

Upgrade options

Hardware that runs Yi 1.5 34B well

RTX A6000 48GBBudget pick

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.30.6 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$4,650 MSRP

RTX PRO 5000 Blackwell 48GBBest value

48 GB VRAM (+16)1344 GB/s (+444)

Raises estimated decode speed by about 87%.59.1 tok/s decode

Raises estimated decode speed by about 87%.

Adds memory headroom for longer context windows and future model growth.

~$4,999 MSRP

NVIDIA L40 48GBNVIDIA upgrade

48 GB VRAM (+16)

Adds memory headroom for longer context windows and future model growth.35.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$5,500 MSRP

Frequently asked questions

Can NVIDIA V100 32GB run Yi 1.5 34B?

Yes, NVIDIA V100 32GB can run Yi 1.5 34B with a B grade (Tight fit). Expected decode speed: 31.6 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 34B?

The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 34B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Yi 1.5 34B achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6133ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on NVIDIA V100 32GB receives a B grade with 31.6 tok/s and 4K context.

What context window can Yi 1.5 34B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for NVIDIA V100 32GB See all hardware for Yi 1.5 34B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

31.6 tok/s

3345 ms

Coding

Tight fit

31.6 tok/s

6133 ms

Agentic Coding

Runs with offload (needs ~0.3 GB host RAM)

26.3 tok/s

10711 ms

Reasoning

Tight fit

31.6 tok/s

7248 ms

RAG

Runs with offload (needs ~0.3 GB host RAM)

26.3 tok/s

13388 ms

Yi 1.5 34B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

62.9

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

RTX 4090 24GB

24 GB

Q4_K_M

21.7

Heavy offload

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 3090 24GB

24 GB

Q4_K_M

18.6

Heavy offload

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

12.6

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

11.5

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

7.8

Too big

RTX 4070 12GB

12 GB

Q4_K_M

3.0

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

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on NVIDIA V100 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B61
Q3_K_S	3	16.7 GB	Low	B62
NVFP4	4	19.0 GB	Medium	B62
Q4_K_M	4	20.7 GB	Medium	B62
Q5_K_MBest for your GPU	5	24.5 GB	High	B61
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

Frequently asked questions

Can NVIDIA V100 32GB run Yi 1.5 34B?

Yes, NVIDIA V100 32GB can run Yi 1.5 34B with a B grade (Tight fit). Expected decode speed: 31.6 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 34B?

The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 34B run at on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Yi 1.5 34B achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6133ms using Q4_K_M quantization.

Can NVIDIA V100 32GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on NVIDIA V100 32GB receives a B grade with 31.6 tok/s and 4K context.

What context window can Yi 1.5 34B use on NVIDIA V100 32GB?

On NVIDIA V100 32GB, Yi 1.5 34B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.