Will It Run AI

Can Yi 1.5 6B Chat run on NVIDIA L4 24GB?

YES — Runs Great

C47Usable
Estimated from fit model

Yi 1.5 6B Chat needs ~8.0 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~53 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 8.0 GB, 53.3 tok/s, Runs well
8.0 GB required24.0 GB available
33% VRAM used

Fit status

Runs well

Decode

53.3 tok/s

TTFT

3634 ms

Safe context

381K

Memory

8.0 GB / 24.0 GB

Memory breakdown

Weights3.7 GB
KV Cache0.7 GB
Runtime1.2 GB
Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 1.5 6B Chat on NVIDIA L4 24GB
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: 53.3 tok/s decode · 3.6s TTFT (warm) · 133 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 well53.3 tok/s1982 ms381K
CodingCRuns well53.3 tok/s3634 ms381K
Agentic CodingCRuns well53.3 tok/s5285 ms381K
ReasoningCRuns well53.3 tok/s4294 ms381K
RAGCRuns well53.3 tok/s6607 ms381K

Quantization options

How Yi 1.5 6B Chat (6B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.3 GB
LowC44
Q3_K_S
3
2.9 GB
LowC44
NVFP4
4
3.4 GB
MediumC44
Q4_K_M
4
3.7 GB
MediumC45
Q5_K_M
5
4.3 GB
HighC45
Q6_K
6
4.9 GB
HighC45
Q8_0
8
6.4 GB
Very HighC46
F16Best for your GPU
16
12.3 GB
MaximumC50

Get started

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

Run

lms load hf-bartowski--yi-1-5-6b-chat-gguf && lms server start

Opções de upgrade

Hardware que roda bem Yi 1.5 6B Chat

NVIDIARTX 5090 32GBOpção econômica
32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 58%.84 tok/s decodificação

Raises estimated decode speed by about 58%.

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

~$1,999 MSRP

NVIDIARTX PRO 4500 Blackwell 32GBMelhor custo-benefício
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 58%.84 tok/s decodificação

Raises estimated decode speed by about 58%.

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

~$2,499 MSRP

NVIDIARTX 5000 Ada 32GBUpgrade NVIDIA
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 58%.84 tok/s decodificação

Raises estimated decode speed by about 58%.

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

~$4,000 MSRP

Frequently asked questions

Can NVIDIA L4 24GB run Yi 1.5 6B Chat?

Yes, NVIDIA L4 24GB can run Yi 1.5 6B Chat with a C grade (Runs well). Expected decode speed: 53.3 tok/s.

How much VRAM does Yi 1.5 6B Chat need?

Yi 1.5 6B Chat (6B parameters) requires approximately 8.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 1.5 6B Chat?

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

What speed will Yi 1.5 6B Chat run at on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi 1.5 6B Chat achieves approximately 53.3 tokens per second decode speed with a time-to-first-token of 3634ms using Q4_K_M quantization.

Can NVIDIA L4 24GB run Yi 1.5 6B Chat for coding?

For coding workloads, Yi 1.5 6B Chat on NVIDIA L4 24GB receives a C grade with 53.3 tok/s and 381K context.

What context window can Yi 1.5 6B Chat use on NVIDIA L4 24GB?

On NVIDIA L4 24GB, Yi 1.5 6B Chat can safely use up to 381K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA L4 24GBSee all hardware for Yi 1.5 6B Chat
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