Will It Run AI

Can Yi 1.5 34B run on RTX 5090 32GB?

YES — Tight Fit

B65Good
Estimated from fit model

Yi 1.5 34B needs ~28.8 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~63 tok/s.

Runtime: OllamaCapacity: TightBandwidth: HighStack: 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) — 28.8 GB, 62.9 tok/s, Tight fit
28.8 GB required32.0 GB available
90% VRAM used

Fit status

Tight fit

Decode

62.9 tok/s

TTFT

3080 ms

Safe context

4K

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 RTX 5090 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: 62.9 tok/s decode · 3.1s TTFT (warm) · 157 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
ChatBTight fit62.9 tok/s1680 ms4K
CodingBTight fit62.9 tok/s3080 ms4K
Agentic CodingBRuns with offload (needs ~0.3 GB host RAM)46.7 tok/s6034 ms4K
ReasoningBTight fit62.9 tok/s3640 ms4K
RAGBRuns with offload (needs ~0.3 GB host RAM)46.7 tok/s7542 ms4K

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowB61
Q3_K_S
3
16.7 GB
LowB62
NVFP4
4
19.0 GB
MediumB62
Q4_K_M
4
20.7 GB
MediumB62
Q5_K_MBest for your GPU
5
24.5 GB
HighB61
Q6_K
6
27.9 GB
HighF0
Q8_0
8
36.4 GB
Very HighF0
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

Opciones de mejora

Hardware que ejecuta bien Yi 1.5 34B

NVIDIARTX PRO 5000 Blackwell 48GBOpción económica
48 GB VRAM (+16)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.59.1 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$4,999 MSRP

NVIDIARTX 6000 Ada 48GBMejor relación calidad-precio
48 GB VRAM (+16)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.41.2 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,800 MSRP

NVIDIANVIDIA A100 40GBMejora NVIDIA
40 GB VRAM (+8)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.68.4 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can RTX 5090 32GB run Yi 1.5 34B?

Yes, RTX 5090 32GB can run Yi 1.5 34B with a B grade (Tight fit). Expected decode speed: 62.9 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 RTX 5090 32GB?

On RTX 5090 32GB, Yi 1.5 34B achieves approximately 62.9 tokens per second decode speed with a time-to-first-token of 3080ms using Q4_K_M quantization.

Can RTX 5090 32GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on RTX 5090 32GB receives a B grade with 62.9 tok/s and 4K context.

What context window can Yi 1.5 34B use on RTX 5090 32GB?

On RTX 5090 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 RTX 5090 32GBSee all hardware for Yi 1.5 34B
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