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Hy3 VRAM Requirements

Tencent, China · Released July 2, 2026

Hy3 has 298.8B parameters (20B active per token). In BF16 its weights alone take 550 GiB; quantized to 4-bit, about 166 GiB. Each 32K-token request adds 10.0 GiB of KV cache. Cheapest way to run it today: 8 × GeForce RTX 3090 (24 GB) at $1.12 per hour. It calls tools and lets you set its reasoning effort.

Size your own setup in the calculator →

Hy3 at a glance

Made by
Tencent, China
Released
July 2, 2026
Parameters
298.8B, 20B active
Context window
256K tokens
License
Apache 2.0, commercial use allowed
Reads
Text only
Tool calling
Supported
Reasoning
Adjustable reasoning effort
Runs with
vLLM, SGLang, llama.cpp (Ollama, LM Studio), MLX (Mac) · vLLM recipe (opens in a new tab)
Checked against

KV cache matches the GGUF header of bartowski/Hy3-GGUF (opens in a new tab) (what llama.cpp allocates), October 17, 2026

VRAM by precision and context length

Weights plus the KV cache of one request (BF16 cache), in GiB. Add 1 to 3 GiB for the inference engine itself.

Weights formatWeights only+ 8K context+ 32K context+ 128K context+ 256K context
BF16 / FP16550552560590630
FP8 / INT8276278286316356
4-bit (GGUF Q4_K_M)166168176206246

Hy3 GGUF files

Exact size of each quantization in bartowski/Hy3-GGUF (opens in a new tab), and the smallest GPU that runs it with llama.cpp, Ollama or LM Studio and an 8K-token context.

QuantizationFile sizeSmallest GPU
Q8_0296 GiBMore than 96 GB: see the calculator
Q6_K240 GiBMore than 96 GB: see the calculator
Q5_K_M198 GiBMore than 96 GB: see the calculator
Q4_K_M170 GiBMore than 96 GB: see the calculator
IQ4_XS150 GiBMore than 96 GB: see the calculator
Q3_K_M128 GiBMore than 96 GB: see the calculator
Q2_K99.4 GiBMore than 96 GB: see the calculator
IQ1_S59.7 GiBRTX PRO 6000 Blackwell (96 GB)
Show the other 18 files
QuantizationFile sizeSmallest GPU
Q5_K_S192 GiBMore than 96 GB: see the calculator
Q4_1175 GiBMore than 96 GB: see the calculator
Q4_K_L170 GiBMore than 96 GB: see the calculator
Q4_K_S163 GiBMore than 96 GB: see the calculator
Q4_0159 GiBMore than 96 GB: see the calculator
IQ4_NL158 GiBMore than 96 GB: see the calculator
IQ3_M134 GiBMore than 96 GB: see the calculator
Q3_K_XL134 GiBMore than 96 GB: see the calculator
Q3_K_L133 GiBMore than 96 GB: see the calculator
IQ3_XS128 GiBMore than 96 GB: see the calculator
Q3_K_S122 GiBMore than 96 GB: see the calculator
IQ3_XXS117 GiBMore than 96 GB: see the calculator
Q2_K_L99.8 GiBMore than 96 GB: see the calculator
IQ2_M95.1 GiBMore than 96 GB: see the calculator
IQ2_S86.4 GiBRTX PRO 6000 Blackwell (96 GB)
IQ2_XS84.8 GiBRTX PRO 6000 Blackwell (96 GB)
IQ2_XXS76.5 GiBRTX PRO 6000 Blackwell (96 GB)
IQ1_M66.2 GiBRTX PRO 6000 Blackwell (96 GB)

Read every day from Hugging Face. Split files are added together; vision projectors are left out.

How many GPUs to run Hy3

Fewest GPUs that fit the weights plus one 8K-token request. Data-center GPUs use vLLM (90% of memory usable), the others llama.cpp. Several GPUs split the model with tensor or pipeline parallelism.

GPUBF16 / FP168-bit4-bit
GeForce RTX 4090 24 GB32 (several machines)16 (several machines)8
GeForce RTX 5090 32 GB24 (several machines)16 (several machines)6
RTX PRO 6000 Blackwell 96 GB632
Mac M5 Max 128 GB 96 GB usable632
Ryzen AI Max+ 395 128 GB 96 GB usable632
DGX Spark (GB10) 128 GB532
NVIDIA H100 80 GB16 (several servers)53
NVIDIA H200 141 GB532
NVIDIA B200 (HGX) 180 GB422

8-bit: FP8 with vLLM (INT8 on GPUs without FP8), Q8_0 with llama.cpp. 4-bit: AWQ / GPTQ with vLLM, Q4_K_M with llama.cpp, both counted at the Q4_K_M size.

Cheapest way to run Hy3 today

For each GPU: the fewest cards that fit Hy3 with one 8K-token request, the most faithful weight format at that count, and the cheapest on-demand price (2026-10-17).

On-demand prices from AWS, Google Cloud, Azure, RunPod, Vast.ai, Oracle Cloud, OVHcloud, Scaleway, Verda, Akash and UpCloud, checked every hour. Serving many users needs more KV cache, so more memory: size it in the calculator. Some links are affiliate links: StudioTV may earn a commission, at no extra cost to you.

AWS, Google Cloud and UpCloud prices: Spare Cores (opens in a new tab), CC BY-SA 4.0 (opens in a new tab), shared under the same license.

Or use Hy3 through an API

No GPU to rent: these providers serve Hy3 and bill per token. Speeds are measured by Hugging Face for one request.

ProviderOutput, per 1M tokensInput, per 1MSpeedFirst token
DeepInfra$0.530$0.13055 tokens/s569 ms

Prices and speeds from Hugging Face Inference Providers (opens in a new tab), read every day (October 17, 2026). An API wins at low volume; renting GPUs wins when they stay busy: the calculator gives the break-even.

Recommended settings for Hy3

Sampling defaults from the official generation_config.json (opens in a new tab) of tencent/Hy3.

SettingValue
Temperature0.9
Top-p1

vLLM and SGLang apply these defaults unless a request sets its own. With llama.cpp, pass them yourself: --temp 0.9 --top-p 1

Reasoning. Adjustable reasoning effort. Reasoning tokens take context and KV cache like any other token: leave room for them.

KV cache: how memory grows with context

Architecture: 80 layers · 80 full attention (8 KV × 128). Every layer keeps keys and values for every token (grouped-query attention), so the cache grows linearly with context. Only 20B parameters are active per token, but all 299B must sit in memory: VRAM depends on the total, speed on the active part.

Context per requestBF16 cacheFP8 cache
8K tokens2.50 GiB1.25 GiB
32K tokens10.0 GiB5.00 GiB
128K tokens40.0 GiB20.0 GiB
256K tokens80.0 GiB40.0 GiB

Hy3 VRAM FAQ

How much VRAM does Hy3 need?

In BF16 the weights alone take 550 GiB (590 GB). In FP8 that is 276 GiB, and about 166 GiB with 4-bit quantization (Q4_K_M). Each request then adds KV cache: 2.50 GiB at 8K tokens and 10.0 GiB at 32K (BF16 cache).

Can Hy3 run on a single RTX 4090 (24 GB)?

No. Even in 4-bit it needs 8 RTX 4090s. In 4-bit it needs 3 H100 GPUs.

Can Hy3 run on an RTX 5090 (32 GB)?

No. Even in 4-bit it needs 6 RTX 5090s.

Can Hy3 run on a Mac?

Yes. In 4-bit (Q4_K_M with llama.cpp) it needs a Mac with 256 GB of unified memory or more, of which macOS gives the GPU about 192 GB, for one request with an 8K-token context. In 8-bit (Q8_0) it needs 512 GB.

Can Hy3 run on a DGX Spark or a Ryzen AI Max+ 395 (128 GB)?

No. Even in 4-bit it needs 2 DGX Spark units or 2 Ryzen AI Max+ 395 machines (128 GB), linked over the network.

What is the cheapest way to run Hy3?

On 2026-10-17, the cheapest on-demand setup is 8 × GeForce RTX 3090 (24 GB) with Q4_K_M weights on Vast.ai, at $1.12 per hour (about $817.60 per month). Next: 4 × RTX A6000 (48 GB) with Q4_K_M weights on Vast.ai, at $1.28 per hour. Sized for one 8K-token request; prices are checked every hour.

How many H100 GPUs does Hy3 need?

With one request and an 8K-token context: 16 in BF16, 5 in FP8 and 3 in 4-bit (vLLM, 90% of memory usable). Above 8 GPUs, that means several servers. Serving many users at once needs more memory for their KV caches: the calculator sizes that for you.

Does Hy3 support tool calling?

Yes, Hy3 supports tool (function) calling: it can ask your application to run a function and use the result.

Does Hy3 have a thinking mode?

Adjustable reasoning effort. Reasoning tokens count toward the context and the KV cache.

What temperature should I use with Hy3?

The official defaults are temperature 0.9, top-p 1.

Hy3 VRAM badge

For a model card or a README: the 4-bit size and the smallest GPU it fits on, linked to this page. Data as JSON: /api/models/hy3.json.

Hy3 VRAM badge

[![Hy3 VRAM](https://studiotvai.com/badge/hy3.svg)](https://studiotvai.com/vram-requirements/hy3)

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Estimates, not guarantees: computed from the official config.json with the same engine as the LLM VRAM Calculator. Real usage depends on your engine version and settings.