Will It Run AI

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 ~23 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: 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) — 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

4K

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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well22.6 tok/s4680 ms4K
CodingBRuns well22.6 tok/s8580 ms4K
Agentic CodingBRuns well22.6 tok/s12479 ms4K
ReasoningBRuns well22.6 tok/s10140 ms4K
RAGBRuns well22.6 tok/s15599 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowC55
Q3_K_S
3
16.7 GB
LowB55
NVFP4
4
19.0 GB
MediumB56
Q4_K_M
4
20.7 GB
MediumB56
Q5_K_M
5
24.5 GB
HighB57
Q6_K
6
27.9 GB
HighB58
Q8_0Best for your GPU
8
36.4 GB
Very HighB60
F16
16
69.7 GB
MaximumF0

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

Opções de upgrade

Hardware que roda bem Yi 1.5 34B

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica
96 GB VRAM (+32)1792 GB/s (+1192)
B
Raises estimated decode speed by about 222%.78.8 tok/s decodificação

Raises estimated decode speed by about 222%.

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

~$9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBMelhor custo-benefício
96 GB VRAM (+32)1597 GB/s (+997)
B
Raises estimated decode speed by about 187%.70.2 tok/s decodificação

Raises estimated decode speed by about 187%.

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

~$9,999 MSRP

NVIDIANVIDIA H20 96GBUpgrade NVIDIA
96 GB VRAM (+32)4000 GB/s (+3400)
B
Raises estimated decode speed by about 592%.169.6 tok/s decodificação

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: 22.6 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 22.6 tokens per second decode speed with a time-to-first-token of 8580ms 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 22.6 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 64GBSee all hardware for Yi 1.5 34B
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