Will It Run AI

Can Yi 1.5 6B run on NVIDIA A2 16GB?

YES — Runs Great

C49Usable
Estimated from fit model

Yi 1.5 6B needs ~7.4 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~46 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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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) — 7.4 GB, 46.4 tok/s, Runs well
7.4 GB required16.0 GB available
46% VRAM used

Fit status

Runs well

Decode

46.4 tok/s

TTFT

4177 ms

Safe context

4K

Memory

7.4 GB / 16.0 GB

Memory breakdown

Weights3.7 GB
KV Cache1.0 GB
Runtime1.2 GB
Headroom1.6 GB

See how fast it feels

See how fast it feelsYi 1.5 6B on NVIDIA A2 16GB
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: 46.4 tok/s decode · 4.2s TTFT (warm) · 116 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 well46.4 tok/s2278 ms4K
CodingCRuns well46.4 tok/s4177 ms4K
Agentic CodingCRuns well46.4 tok/s6075 ms4K
ReasoningCRuns well46.4 tok/s4936 ms4K
RAGCRuns well46.4 tok/s7594 ms4K

Quantization options

How Yi 1.5 6B (6B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC46
Q3_K_S
3
2.9 GB
LowC47
NVFP4
4
3.4 GB
MediumC47
Q4_K_M
4
3.7 GB
MediumC47
Q5_K_M
5
4.3 GB
HighC48
Q6_K
6
4.9 GB
HighC48
Q8_0
8
6.4 GB
Very HighC50
F16Best for your GPU
16
12.3 GB
MaximumC50

Get started

Copy-paste commands to run Yi 1.5 6B on your machine.

Run

lms load Yi-1.5-6B-Chat && lms server start

Opções de upgrade

Hardware que roda bem Yi 1.5 6B

NVIDIARTX 4000 Ada 20GBOpção econômica
20 GB VRAM (+4)360 GB/s (+160)
C
Raises estimated decode speed by about 80%.83.4 tok/s decodificação

Raises estimated decode speed by about 80%.

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

~$1,250 MSRP

NVIDIARTX A4500 20GBMelhor custo-benefício
20 GB VRAM (+4)640 GB/s (+440)
C
Raises estimated decode speed by about 81%.84 tok/s decodificação

Raises estimated decode speed by about 81%.

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

~$2,000 MSRP

Frequently asked questions

Can NVIDIA A2 16GB run Yi 1.5 6B?

Yes, NVIDIA A2 16GB can run Yi 1.5 6B with a C grade (Runs well). Expected decode speed: 46.4 tok/s.

How much VRAM does Yi 1.5 6B need?

Yi 1.5 6B (6B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B?

The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 1.5 6B run at on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Yi 1.5 6B achieves approximately 46.4 tokens per second decode speed with a time-to-first-token of 4177ms using Q4_K_M quantization.

Can NVIDIA A2 16GB run Yi 1.5 6B for coding?

For coding workloads, Yi 1.5 6B on NVIDIA A2 16GB receives a C grade with 46.4 tok/s and 4K context.

What context window can Yi 1.5 6B use on NVIDIA A2 16GB?

On NVIDIA A2 16GB, Yi 1.5 6B 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 A2 16GBSee all hardware for Yi 1.5 6B
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