Can Yi 1.5 34B run on RTX A5000 24GB?

BARELY — Tight on Memory

C51Usable

Estimated from fit model

Yi 1.5 34B needs ~27.7 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload

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) — 27.7 GB, 15.6 tok/s, Very compromised (needs ~2.8 GB host RAM)

27.7 GB required24.0 GB available

115% VRAM needed

3.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~2.8 GB host RAM)

Decode

15.6 tok/s

TTFT

12406 ms

Safe context

Memory

27.7 GB / 24.0 GB

Offload

10%

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on RTX A5000 24GB
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: 15.6 tok/s decode · 12.4s TTFT (warm) · 39 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~1.5 GB host RAM)	18.0 tok/s	5860 ms	4K
Coding	C	Very compromised (needs ~2.8 GB host RAM)	15.6 tok/s	12406 ms	4K
Agentic Coding	F	Too heavy	12.0 tok/s	23434 ms	4K
Reasoning	C	Very compromised (needs ~2.8 GB host RAM)	15.6 tok/s	14661 ms	4K
RAG	F	Too heavy	12.0 tok/s	29293 ms

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on RTX A5000 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	B63
Q3_K_SBest for your GPU	3	16.7 GB	Low	B62

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 5090 32GBBudget pick

32 GB VRAM (+8)1792 GB/s (+1024)

Removes host-memory offload, which is usually the single biggest latency and throughput win.40.7 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 161%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBBest value

32 GB VRAM (+8)896 GB/s (+128)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 153%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA upgrade

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.24.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 54%.

~$4,000 MSRP

Frequently asked questions

Can RTX A5000 24GB run Yi 1.5 34B?

Yes, RTX A5000 24GB can run Yi 1.5 34B with a C grade (Very compromised (needs ~2.8 GB host RAM)). Expected decode speed: 15.6 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 27.7 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 A5000 24GB?

On RTX A5000 24GB, Yi 1.5 34B achieves approximately 15.6 tokens per second decode speed with a time-to-first-token of 12406ms using Q4_K_M quantization.

Can RTX A5000 24GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on RTX A5000 24GB receives a C grade with 15.6 tok/s and 4K context.

What context window can Yi 1.5 34B use on RTX A5000 24GB?

On RTX A5000 24GB, 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.

What should I upgrade first if Yi 1.5 34B feels slow on RTX A5000 24GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX A5000 24GB See all hardware for Yi 1.5 34B

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