Can Yi 1.5 34B run on RTX PRO 4000 Blackwell 24GB?

BARELY — Tight on Memory

C51Usable

Estimated from fit model

Yi 1.5 34B needs ~27.7 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~17 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, 16.9 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

16.9 tok/s

TTFT

11480 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 PRO 4000 Blackwell 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: 16.9 tok/s decode · 11.5s TTFT (warm) · 42 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)	19.4 tok/s	5447 ms	4K
Coding	C	Very compromised (needs ~2.8 GB host RAM)	16.9 tok/s	11480 ms	4K
Agentic Coding	F	Too heavy	13.1 tok/s	21512 ms	4K
Reasoning	C	Very compromised (needs ~2.8 GB host RAM)	16.9 tok/s	13568 ms	4K
RAG	F	Too heavy	13.1 tok/s	26890 ms	4K

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on RTX PRO 4000 Blackwell 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
NVFP4	4	19.0 GB	Medium	F0
Q4_K_M	4	20.7 GB	Medium	F0
Q5_K_M	5	24.5 GB	High	F0
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0

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

RTX 5090 32GBOpção econômica

32 GB VRAM (+8)1792 GB/s (+1120)

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

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

Raises estimated decode speed by about 141%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GBMelhor custo-benefício

32 GB VRAM (+8)896 GB/s (+224)

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

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

Raises estimated decode speed by about 133%.

~$2,499 MSRP

RTX 5000 Ada 32GBUpgrade NVIDIA

32 GB VRAM (+8)

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

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

Raises estimated decode speed by about 43%.

~$4,000 MSRP

Frequently asked questions

Can RTX PRO 4000 Blackwell 24GB run Yi 1.5 34B?

Yes, RTX PRO 4000 Blackwell 24GB can run Yi 1.5 34B with a C grade (Very compromised (needs ~2.8 GB host RAM)). Expected decode speed: 16.9 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 PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 24GB, Yi 1.5 34B achieves approximately 16.9 tokens per second decode speed with a time-to-first-token of 11480ms using Q4_K_M quantization.

Can RTX PRO 4000 Blackwell 24GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on RTX PRO 4000 Blackwell 24GB receives a C grade with 16.9 tok/s and 4K context.

What context window can Yi 1.5 34B use on RTX PRO 4000 Blackwell 24GB?

On RTX PRO 4000 Blackwell 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 PRO 4000 Blackwell 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 PRO 4000 Blackwell 24GB See all hardware for Yi 1.5 34B

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