Can Yi 1.5 34B run on NVIDIA A16 64GB?

YES — Runs Great

B61Good

Estimated from fit model

Yi 1.5 34B needs ~32.0 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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) — 32.0 GB, 24.5 tok/s, Runs well

32.0 GB required64.0 GB available

50% VRAM used

Fit status

Runs well

Decode

24.5 tok/s

TTFT

7902 ms

Safe context

Memory

32.0 GB / 64.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on NVIDIA A16 64GB
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: 24.5 tok/s decode · 7.9s TTFT (warm) · 61 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	Runs well	24.5 tok/s	4310 ms	4K
Coding	B	Runs well	24.5 tok/s	7902 ms	4K
Agentic Coding	B	Runs well	24.5 tok/s	11494 ms	4K
Reasoning	B	Runs well	24.5 tok/s	9339 ms	4K
RAG	B	Runs well	24.5 tok/s	14368 ms	4K

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C55
Q3_K_S	3	16.7 GB	Low	B55
NVFP4	4	19.0 GB	Medium	B56
Q4_K_M	4	20.7 GB	Medium	B56
Q5_K_M	5	24.5 GB	High	B57
Q6_K	6	27.9 GB	High	B58
Q8_0Best for your GPU	8	36.4 GB	Very High	B60
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 PRO 6000 Blackwell Workstation Edition 96GBBudget pick

96 GB VRAM (+32)1792 GB/s (+1192)

Raises estimated decode speed by about 222%.78.8 tok/s decode

Raises estimated decode speed by about 222%.

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

~$9,999 MSRP

RTX PRO 6000 Blackwell Server Edition 96GBBest value

96 GB VRAM (+32)1597 GB/s (+997)

Raises estimated decode speed by about 187%.70.2 tok/s decode

Raises estimated decode speed by about 187%.

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

~$9,999 MSRP

NVIDIA H20 96GBNVIDIA upgrade

96 GB VRAM (+32)4000 GB/s (+3400)

Raises estimated decode speed by about 592%.169.6 tok/s decode

Raises estimated decode speed by about 592%.

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

~$12,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run Yi 1.5 34B?

Yes, NVIDIA A16 64GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 24.5 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 32.0 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 A16 64GB?

On NVIDIA A16 64GB, Yi 1.5 34B achieves approximately 24.5 tokens per second decode speed with a time-to-first-token of 7902ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on NVIDIA A16 64GB receives a B grade with 24.5 tok/s and 4K context.

What context window can Yi 1.5 34B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 A16 64GB See all hardware for Yi 1.5 34B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

24.5 tok/s

4310 ms

Coding

Runs well

24.5 tok/s

7902 ms

Agentic Coding

Runs well

24.5 tok/s

11494 ms

Reasoning

Runs well

24.5 tok/s

9339 ms

RAG

Runs well

24.5 tok/s

14368 ms

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C55
Q3_K_S	3	16.7 GB	Low	B55
NVFP4	4	19.0 GB	Medium	B56
Q4_K_M	4	20.7 GB	Medium	B56
Q5_K_M	5	24.5 GB	High	B57
Q6_K	6	27.9 GB	High	B58
Q8_0Best for your GPU	8	36.4 GB	Very High	B60
F16	16	69.7 GB	Maximum	F0

Frequently asked questions

Can NVIDIA A16 64GB run Yi 1.5 34B?

Yes, NVIDIA A16 64GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 24.5 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 32.0 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 A16 64GB?

On NVIDIA A16 64GB, Yi 1.5 34B achieves approximately 24.5 tokens per second decode speed with a time-to-first-token of 7902ms using Q4_K_M quantization.

Can NVIDIA A16 64GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on NVIDIA A16 64GB receives a B grade with 24.5 tok/s and 4K context.

What context window can Yi 1.5 34B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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.