Can Yi 1.5 34B run on RTX PRO 6000 Blackwell Workstation Edition 96GB?

YES — Runs Great

B62Good

Estimated from fit model

Yi 1.5 34B needs ~35.2 GB VRAM. RTX PRO 6000 Blackwell Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~79 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

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) — 35.2 GB, 78.8 tok/s, Runs well

35.2 GB required96.0 GB available

37% VRAM used

Fit status

Runs well

Decode

78.8 tok/s

TTFT

2457 ms

Safe context

Memory

35.2 GB / 96.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsYi 1.5 34B on RTX PRO 6000 Blackwell Workstation Edition 96GB
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: 78.8 tok/s decode · 2.5s TTFT (warm) · 197 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	78.8 tok/s	1340 ms	4K
Coding	B	Runs well	78.8 tok/s	2457 ms	4K
Agentic Coding	B	Runs well	78.8 tok/s	3574 ms	4K
Reasoning	B	Runs well	78.8 tok/s	2904 ms	4K
RAG	B	Runs well	78.8 tok/s	4467 ms	4K

Inference speed

Yi 1.5 34B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 1.5 34B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~63 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	62.9	Tight
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	31.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	31.4	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	29.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	24.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	23.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	21.7	Heavy offload
RX 7900 XTX 24GB	24 GB	Q4_K_M	20.1	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	19.8	Tight
RTX 3090 24GB	24 GB	Q4_K_M	18.6	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	12.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	11.5	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	7.8	Too big
RTX 4070 12GB	12 GB	Q4_K_M	3.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi 1.5 34B (34B params) fits at each quantization level on RTX PRO 6000 Blackwell Workstation Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C52
Q3_K_S	3	16.7 GB	Low	C53
NVFP4	4	19.0 GB	Medium	C53
Q4_K_M	4	20.7 GB	Medium	C53
Q5_K_M	5	24.5 GB	High	C54
Q6_K	6	27.9 GB	High	C54
Q8_0	8	36.4 GB	Very High	B56
F16Best for your GPU	16	69.7 GB	Maximum	B60

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

Frequently asked questions

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Yi 1.5 34B?

Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run Yi 1.5 34B with a B grade (Runs well). Expected decode speed: 78.8 tok/s.

How much VRAM does Yi 1.5 34B need?

Yi 1.5 34B (34B parameters) requires approximately 35.2 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 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, Yi 1.5 34B achieves approximately 78.8 tokens per second decode speed with a time-to-first-token of 2457ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Workstation Edition 96GB run Yi 1.5 34B for coding?

For coding workloads, Yi 1.5 34B on RTX PRO 6000 Blackwell Workstation Edition 96GB receives a B grade with 78.8 tok/s and 4K context.

What context window can Yi 1.5 34B use on RTX PRO 6000 Blackwell Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, 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 PRO 6000 Blackwell Workstation Edition 96GB See all hardware for Yi 1.5 34B

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