01.AIYiLlamaForCausalLM

Yi 34B Chat — Hardware Requirements & GPU Compatibility

Chat

Yi-34B-Chat is 01.AI's instruction-tuned chat model built on the 34-billion-parameter Yi-34B base, a bilingual English/Chinese model trained from scratch on roughly 3 trillion tokens. At launch in late 2023 it placed second only to GPT-4 Turbo on the AlpacaEval leaderboard among a field that included GPT-4, Mixtral, and Claude, an unusually strong result for an open bilingual model of its generation. Running the full-precision checkpoint needs a multi-GPU workstation or a single high-end data-center card, but the official 4-bit quantized release fits a single consumer GPU. A separate Yi-34B-200K sibling extends the same base model's context far beyond this chat version. Context length is 4,096 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in November 2023, making it one of the earlier open 34B-class chat models rather than a current-generation release.

34.4B params · dense4K contextApache 2.0Nov 202323.4K downloads358 likes5.1K quant downloads

Your hardware

Verdicts below are for the reference RTX 4090. Set yours to personalise the page.

Detection reads your browser's GPU name with no data sent anywhere.

B

Decent on a 24 GB GPU

Runs from 12 GB (IQ2_XS · 4K)Best on 24 GB (Q4_K_M · ~31 tok/s · 4K)Macs from 16 GB (IQ2_S · ~12 tok/s)

Apache 2.0

Reference GPU · RTX 4090 24 GB

Estimates, ±20% · how we estimate

Run Yi 34B Chat

One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.

Recommended

Ollama

one command · needs Ollama installed

$ollama run hf.co/mradermacher/Yi-34B-Chat-i1-GGUF:Q4_K_M

Q4_K_M fits fully on the RTX 4090 · 2.1 GB free · 4K context

Download: Yi-34B-Chat.i1-Q4_K_M.gguf20.66 GBmradermacher

install, OS, other quants

Ways to runOther ways

each opens step-by-step

Run in cloud

Your own private copy · Q4_K_M · 24 GB GPU

The easiest way to try this model
Running in a few minutes, easy setup
Chat right here or connect any app

$5+ bonus for new users

How Much VRAM Does Yi 34B Chat Need?

Select a quantization level; the listed hardware adheres to it.

Quantization	VRAM	+32K	+128K	File	Quality	On RTX 4090 (reference)
	10.3 GB	—	—	9.3 GB	2-bit · i-quant extreme compression — Importance-weighted 2-bit, extreme compression — significant quality loss	Fits: 4K context · ~64 tok/s
	11.1 GB	—	—	10.3 GB	2-bit · i-quant extra small — Importance-weighted 2-bit, extra small	Fits: 4K context · ~59 tok/s
	11.6 GB	—	—	10.9 GB	2-bit · i-quant small — Importance-weighted 2-bit, small	Fits: 4K context · ~57 tok/s
	12.4 GB	—	—	11.8 GB	2-bit · i-quant medium — Importance-weighted 2-bit, medium	Fits: 4K context · ~53 tok/s
	14.1 GB	—	—	13.3 GB	3-bit · i-quant — Importance-weighted 3-bit	Fits: 4K context · ~46 tok/s
	14.6 GB	—	—	11.9 GB	2-bit · small K-quant — 2-bit small K-quant	Fits: 4K context · ~45 tok/s
	15.0 GB	—	—	14.2 GB	3-bit · i-quant extra small — Importance-weighted 3-bit, extra small	Fits: 4K context · ~44 tok/s
	15.4 GB	—	—	15 GB	3-bit · i-quant small — Importance-weighted 3-bit, small	Fits: 4K context · ~43 tok/s
	15.4 GB	—	—	12.8 GB	2-bit · K-quant — 2-bit quantization with K-quant improvements	Fits: 4K context · ~43 tok/s
	15.8 GB	—	—	15 GB	3-bit small — 3-bit small quantization	Fits: 4K context · ~41 tok/s
	16.3 GB	—	—	15.6 GB	3-bit · i-quant medium — Importance-weighted 3-bit, medium	Fits: 4K context · ~40 tok/s
	17.6 GB	—	—	16.7 GB	3-bit medium — 3-bit medium quantization	Fits: 4K context · ~37 tok/s
	18 GB	—	—	19.5 GB	4-bit · legacy — 4-bit legacy quantization	Fits: 4K context · ~36 tok/s
	18.4 GB	—	—	18.1 GB	3-bit large — 3-bit large quantization	Fits: 4K context · ~36 tok/s
	19.3 GB	—	—	18.5 GB	4-bit · i-quant compact — Importance-weighted 4-bit, compact	Fits: 4K context · ~34 tok/s
	20.1 GB	—	—	19.6 GB	4-bit small — 4-bit small quantization	Fits: 4K context · ~33 tok/s
	21.4 GB	—	—	20.7 GB	4-bit medium — 4-bit medium quantization — most popular sweet spot	Fits: 4K context · ~31 tok/s
	22.3 GB	—	—	23.7 GB	5-bit · legacy — 5-bit legacy quantization	Fits: 4K context · ~29 tok/s
	24.4 GB	—	—	23.7 GB	5-bit small — 5-bit small quantization	Does not fit: needs 24.5 GB
	25.3 GB	—	—	24.3 GB	5-bit medium — 5-bit medium quantization — good quality/size tradeoff	Does not fit: needs 25.3 GB
	29.2 GB	—	—	28.2 GB	6-bit, very good — 6-bit quantization, very good quality	Does not fit: needs 29.2 GB
	35.2 GB	—	—	36.5 GB	8-bit, near-lossless — 8-bit quantization, near-lossless	Does not fit: needs 35.2 GB
	69.6 GB	—	—	~68.8 GB	16-bit · bfloat — Brain floating point 16 — preferred for training	Does not fit: needs 69.6 GB

est.calculated, no published file yet

Selected Q4_K_M

4-bit medium quantization

21.9GBat 4K context

Weights: 20.6 GB
KV cache: 1.0 GB
Overhead: 0.3 GB

Fits RTX 4090 (reference) · 2.1 GB free · ~31 tok/s

≥50 tok/s needs ≥1.65 TB/s · RTX 5090 ~54

Or .

Which Hardware Can Run Yi 34B Chat?

Q4_K_M · 21.4 GB

Yi 34B Chat (Q4_K_M) requires 21.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 4K context window can add up to 0.5 GB, bringing total usage to 21.9 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090. 100 devices with unified memory can run Yi 34B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).

By VRAM tier · typical GPUs and Macs at Q4_K_M

Typical hardwareQuantVRAMtok/sContext that fits

8 GB · RTX 4060Doesn't fit — smallest quant needs 10.8 GB8 GB tier →

12 GB · RTX 3060 12GBIQ2_XS · 11.6 GB · ~21 tok/s · 4K11.6~214K12 GB tier →

16 GB · RTX 5060 Ti 16GBIQ3_S · 15.9 GB · ~19 tok/s · 4K15.9~194K16 GB tier →

24 GB · RTX 4090referenceQ4_K_M · 21.9 GB · ~31 tok/s · 4K21.9~314K24 GB tier →

Mac 32 GB · Mac Mini M4Q4_K_M · 21.9 GB · ~4 tok/s · 4K21.9~44KMacs →

Mac 64 GB · Mac Studio M4 MaxQ4_K_M · 21.9 GB · ~18 tok/s · 4K21.9~184KMacs →

How good is Yi 34B Chat?

llmrun Score blends public benchmark boards into one number; 100 = the average open model.

Not enough data yet for an llmrun Score.

A Score needs results on enough independent boards. How the Composite Score is built

Benchmarks

GPQA Diamond
Epoch AI · Nov 2023Independent
14.7%
#98/100 open
Independent
LMArena Text
LMArena · Oct 2026Arena
1183.8
#152/188 open
Arena
MATH Level 5
Epoch AI · Nov 2023Independent
5.1%
#37/39 open
Independent
MMLU
Epoch AI · Nov 2023Lab-reported
73.5%
#23/85 open
Lab-reported
BIG-Bench Hard
Epoch AI · Nov 2023Lab-reported
71.7%
#7/38 open
Lab-reported
GSM8K
Epoch AI · Nov 2023Lab-reported
76.0%
#15/62 open
Lab-reported

All 6 Yi 34B Chat benchmarks

Alternatives to Yi 34B Chat

Yi family · 6 models →

Similar models

6tok/s on RTX 4090

Model	Score	VRAM	tok/s	Why it is similar
Nanbeige4.3 33BNanbeige	—	23.7 GB	~32	same size
Sage A1.5InternScience	—	22 GB	~151	same size
MiniCPM V 4.7 35B A3BOpenBMB	—	22.1 GB	~169	same size
Llm Jp 4.1 32B A3b Thinkingllm-jp	—	20.1 GB	~188	same size
Llm Jp 4.1 33B Thinkingllm-jp	—	22.5 GB	~35	same size
Agnes 3.0 FlashAgnes-AI	—	22.1 GB	~35	same size

Specifications

Publisher: 01.AI
Family: Yi
Parameters: 34.4B
Architecture: LlamaForCausalLM
Context Length: 4,096 tokens
Vocabulary Size: 64,000
Release Date: 2023-11-22
License: Apache 2.0

Resources

Frequently Asked Questions

How much VRAM does Yi 34B Chat need?

Yi 34B Chat requires 21.4 GB of VRAM at Q4_K_M, or 69.6 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 34.4B × 4.8 bits ÷ 8 = 20.6 GB

KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)

Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.

KV Cache + Overhead ≈ 1.3 GB (at full 4K context)

VRAM usage by quantization

Q4_K_M

21.4 GB

Q4_K_M + full context

21.9 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Yi 34B Chat?

Yes, at Q5_0 (22.3 GB) or lower. Higher quantizations like Q5_K_S (24.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Yi 34B Chat?

For Yi 34B Chat, Q4_K_M (21.4 GB) offers the best balance of quality and VRAM usage. Q5_0 (22.3 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 10.3 GB.

VRAM requirement by quantization

IQ2_XXS

10.3 GB~53%

IQ3_XS

15.0 GB~73%

Q3_K_M

17.6 GB~83%

Q4_K_M ★

21.4 GB~89%

Q5_0

22.3 GB~90%

BF16

69.6 GB~100%

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Yi 34B Chat on a Mac?

Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Yi 34B Chat, the smallest at IQ2_S. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 21.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M1 Max (32 GB).

Can I run Yi 34B Chat locally?

Yes — Yi 34B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Yi 34B Chat?

At Q4_K_M, Yi 34B Chat can reach ~224 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~31 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B200 → 8000 ÷ 21.4 × 0.65 = ~243 tok/s

Estimated speed at Q4_K_M (21.4 GB)

NVIDIA B200

~243 tok/s

NVIDIA GeForce RTX 4090

~31 tok/s

NVIDIA B300

~243 tok/s

AMD Instinct MI350X

~224 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Yi 34B Chat?

At Q4_K_M, the download is about 20.63 GB. The full-precision BF16 version is 68.78 GB. The smallest option (IQ2_XXS) is 9.46 GB.

Which GPUs can run Yi 34B Chat?

7 consumer GPUs can run Yi 34B Chat at Q4_K_M. Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.

Which devices can run Yi 34B Chat?

100 devices with unified memory can run Yi 34B Chat at Q4_K_M (21.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.

Run Yi 34B Chat

21.9 GB VRAM at 4K context · fits the RTX 4090 (2.1 GB free)

Works on macOS, Windows and Linux.

Install Ollama (Linux)
$curl -fsSL https://ollama.com/install.sh | sh
Install Ollama (macOS / Windows)
Download and run the app; it installs the ollama command.
Download Ollama ↗
Run Q4_K_M
$ollama run hf.co/mradermacher/Yi-34B-Chat-i1-GGUF:Q4_K_M
Raise the context window
Type it inside the chat. For the API server instead, start it with OLLAMA_CONTEXT_LENGTH=4096 ollama serve.
$/set parameter num_ctx 4096
A larger context needs more memory than the weights alone.
Use the API
Ollama serves its API on http://localhost:11434.

Works on macOS, Windows and Linux.

Install LM Studio
Desktop app for macOS (Apple silicon), Windows and Linux.
lmstudio.ai ↗
Download the model
In the app press Ctrl/Cmd + Shift + M and paste https://huggingface.co/mradermacher/Yi-34B-Chat-i1-GGUF, then pick Q4_K_M. Or use the CLI (run LM Studio once first):
$lms get https://huggingface.co/mradermacher/Yi-34B-Chat-i1-GGUF@Q4_K_M
Open on Hugging Face ↗
Load and chat
Open the model loader, select the model and set Context Length to 4096. Leave GPU offload at maximum if it fits.
Start the local server (optional)
$lms server start

Works on macOS, Windows and Linux.

Install llama.cpp (macOS)
MacPorts, Nix and conda-forge also work.
$brew install llama.cpp
Install options ↗
Install llama.cpp (Windows)
$winget install llama.cpp
Install llama.cpp (Linux)
Download the prebuilt llama-*-bin-ubuntu-* archive for your GPU backend (CPU, Vulkan) from the releases page, unpack it and run the binaries from that folder. Homebrew (brew install llama.cpp) and conda-forge also work on Linux.
llama.cpp releases ↗
Start the server
Downloads the model on first run. The chat UI and an OpenAI-compatible API are at http://localhost:8080.
$llama-server -hf mradermacher/Yi-34B-Chat-i1-GGUF:Q4_K_M -c 4096 -ngl 99 --port 8080

Works on macOS, Windows and Linux.

Option A: Docker Model Runner
Docker Desktop: Settings > AI > Enable Docker Model Runner. Docker Engine on Linux: install the docker-model-plugin package.
$docker model run hf.co/mradermacher/Yi-34B-Chat-i1-GGUF:Q4_K_M
Model Runner docs ↗
The quant tag after the repo is how Hugging Face cards write it; if your Docker version rejects it, drop the tag and pick the quant by repo.
Option B: llama.cpp server image (NVIDIA)
Needs the NVIDIA Container Toolkit. Models are cached in the llama-cache volume. Open http://localhost:8080.
$docker run --gpus all -p 8080:8080 -v llama-cache:/root/.cache ghcr.io/ggml-org/llama.cpp:server-cuda -hf mradermacher/Yi-34B-Chat-i1-GGUF:Q4_K_M -c 4096 -ngl 99 --host 0.0.0.0 --port 8080
Image tags (cuda13, vulkan, rocm) ↗
Cache path inside the image is not in the docs; remove the -v flag if the volume complains.

Linux only.

Install vLLM
Linux only; on Windows run it inside WSL2. NVIDIA GPU shown; AMD ROCm, Intel and CPU builds have their own install pages.
$uv venv --python 3.12 --seed
$source .venv/bin/activate
$uv pip install vllm --torch-backend=auto
vLLM installation ↗
Serve the model
OpenAI-compatible API at http://localhost:8000/v1.
$vllm serve 01-ai/Yi-34B-Chat --max-model-len 4096
Runs the original weights, not the Q4_K_M GGUF; needs ~76 GB of GPU memory.

Works on macOS, Windows and Linux.

Install Jan
jan.ai ↗
Find the model
Open the Hub tab, paste this model ID in Search, and choose Q4_K_M if offered.
$mradermacher/Yi-34B-Chat-i1-GGUF
Use the local API (optional)
Settings > Local API Server > Start Server. It listens on http://127.0.0.1:1337.

Rent GPUs hourly to run Q4_K_M right there. Pricing comes from partner marketplaces and varies.

Available GPU rentals for Q4_K_M, prioritized by value
GPU	Speed	$/1M tokens	Rent
RTX 5090 (32 GB)CheapestCheapest10 GB free	~54 tok/s	$2.54	Deploy on Vast.aifrom $0.50/hr (opens in new tab)	32 GB · 10 GB free · ~54 tok/s · $2.54 / 1M tokens
RTX PRO 4500 (32 GB)10 GB free	~27 tok/s	$3.47	Deploy on RunPodfrom $0.34/hr (opens in new tab)	32 GB · 10 GB free · ~27 tok/s · $3.47 / 1M tokens
A40 (48 GB)26 GB free	~21 tok/s	$4.61	Deploy on RunPodfrom $0.35/hr (opens in new tab)	48 GB · 26 GB free · ~21 tok/s · $4.61 / 1M tokens
RTX PRO 4500 (32 GB)10 GB free	~27 tok/s	$3.63	Deploy on Vast.aifrom $0.36/hr (opens in new tab)	32 GB · 10 GB free · ~27 tok/s · $3.63 / 1M tokens
RTX A6000 (48 GB)26 GB free	~23 tok/s	$4.82	Deploy on Vast.aifrom $0.40/hr (opens in new tab)	48 GB · 26 GB free · ~23 tok/s · $4.82 / 1M tokens
A100 40GB18 GB free	~47 tok/s	$3.60	Deploy on Vast.aifrom $0.61/hr (opens in new tab)	18 GB free · ~47 tok/s · $3.60 / 1M tokens
L40 (48 GB)26 GB free	~26 tok/s	$7.32	Deploy on RunPodfrom $0.69/hr (opens in new tab)	48 GB · 26 GB free · ~26 tok/s · $7.32 / 1M tokens
RTX 5090 (32 GB)10 GB free	~54 tok/s	$3.53	Deploy on RunPodfrom $0.69/hr (opens in new tab)	32 GB · 10 GB free · ~54 tok/s · $3.53 / 1M tokens

Compare GPUs →

Your own private copy of Yi 34B Chat · Q4_K_M · OpenAI-compatible API

Open the llama.cpp listing on RunPod
Open on RunPodServerless (opens in new tab)
New to RunPod? Sign up and add credit first.
2
Fill in the form
Press Deploy and fill in:
Model
TheBloke/Yi-34B-Chat-GGUF
Quantization
Q4_K_M
Context Size
16384
After pasting the model press Esc — don’t pick a suggestion. Press Next.
Create the endpoint
Keep the default 24 GB and 48 GB, then Create Endpoint. Right away: Manage → Edit endpoint → Max workers 1 → Save (changing settings later restarts the worker).
The first start downloads the model, usually 5–15 min. After that, replies after a pause come in seconds.
Create an API key
RunPod → Settings → API Keys → Create. Pick “Restricted” with access to this endpoint only.

Use it

Copy the endpoint URL from its page on RunPod (any of its links works) and paste it with the key. Both stay in this browser.

Endpoint URL

API key

Chats and keys stay only in this browser.

https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1

Put your endpoint ID in place of <ENDPOINT_ID>. The API key is the one from step 2.

from openai import OpenAI
client = OpenAI(base_url="https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1", api_key="rpa_...")
r = client.chat.completions.create(
    model="any",
    messages=[{"role": "user", "content": "Hello!"}])
print(r.choices[0].message.content)

Works with any OpenAI client.

https://api.runpod.ai/v2/<ENDPOINT_ID>/openai/v1

Put your endpoint ID in place of <ENDPOINT_ID>. The API key is the one from step 2.

Free on Windows, macOS, Linux, iOS, Android and the web.

Open the web app ↗ (opens in a new tab)Download ↗ (opens in a new tab)

1. Settings → Model Provider → Add → Add Custom Provider
2. API Mode OpenAI API Compatible; paste the API key and the base URL as API Host
3. Model → Fetch, pick the model
4. New chat → pick the provider → start chatting

Free and open source for Windows, macOS and Linux.

Download Jan ↗ (opens in a new tab)

1.Settings → next to Model Providers press +
2.API format OpenAI-compatible; Base URL is the base URL above (with /v1); API key is your key; Create
3.If no model appears, click + in Models and type any
4.New chat → pick the model → start chatting