Can Yi 34B Chat run on Radeon AI PRO R9700 32GB?

YES — Tight Fit

C50Usable

○Estimated from fit model

Yi 34B Chat needs ~28.5 GB VRAM. Radeon AI PRO R9700 32GB has 32.0 GB. With Q4_K_M quantization, expect ~20 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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) — 28.5 GB, 19.8 tok/s, Tight fit

28.5 GB required32.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

19.8 tok/s

TTFT

9794 ms

Safe context

31K

Memory

28.5 GB / 32.0 GB

Memory breakdown

Weights20.7 GB

KV Cache3.7 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsYi 34B Chat on Radeon AI PRO R9700 32GB
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: 19.8 tok/s decode · 9.8s TTFT (warm) · 49 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	C	Tight fit	19.8 tok/s	5342 ms	31K
Coding	C	Tight fit	19.8 tok/s	9794 ms	31K
Agentic Coding	C	Runs with offload (needs ~0.1 GB host RAM)	15.0 tok/s	18825 ms	31K
Reasoning	C	Tight fit	19.8 tok/s	11575 ms	31K
RAG	C	Runs with offload (needs ~0.1 GB host RAM)	15.0 tok/s	23531 ms	31K

Quantization options

How Yi 34B Chat (34B params) fits at each quantization level on Radeon AI PRO R9700 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.3 GB	Low	C49
Q3_K_S	3	16.7 GB	Low	C51
NVFP4	4	19.0 GB	Medium	C51
Q4_K_M	4	20.7 GB	Medium	C50
Q5_K_MBest for your GPU	5	24.5 GB	High	C50
Q6_K	6	27.9 GB	High	F0
Q8_0	8	36.4 GB	Very High	F0
F16	16	69.7 GB	Maximum	F0