willitrun·ai

Can Hy3 run on Mac Studio M3 Ultra 256GB?

BARELY — Tight on Memory

A76Great
Estimated from fit model

Hy3 needs ~213.4 GB VRAM. Mac Studio M3 Ultra 256GB has 184.3 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 213.4 GB, 11.6 tok/s, Very compromised (needs ~24.5 GB host RAM)
213.4 GB required184.3 GB available
116% VRAM needed

29.1 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~24.5 GB host RAM)

Decode

11.6 tok/s

TTFT

16619 ms

Safe context

4K

Memory

213.4 GB / 184.3 GB

Offload

10%

Memory breakdown

Weights180.0 GB
KV Cache4.9 GB
Runtime0.9 GB
Headroom27.6 GB

See how fast it feels

See how fast it feelsHy3 on Mac Studio M3 Ultra 256GB
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: 11.6 tok/s decode · 16.6s TTFT (warm) · 29 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.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

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 24.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatAVery compromised (needs ~22.7 GB host RAM)11.8 tok/s8930 ms4K
CodingAVery compromised (needs ~24.5 GB host RAM)11.6 tok/s16619 ms4K
Agentic CodingAVery compromised (needs ~28 GB host RAM)11.3 tok/s24883 ms4K
ReasoningAVery compromised (needs ~24.5 GB host RAM)11.6 tok/s19641 ms4K
RAGAVery compromised (needs ~28 GB host RAM)11.3 tok/s31103 ms4K

Inference speed

Hy3 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Hy3 at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is Mac Studio M3 Ultra 256GB at ~12 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 / MacMemoryQuantSpeed (tok/s)Fits?
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M11.6Heavy offload
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M5.5Too big
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M5.2Too big
MacBook Pro M4 Max 128GB
128 GBQ4_K_M4.1Too big
MacBook Pro M4 Max 64GB
64 GBQ4_K_M4.1Too big
NVIDIARTX 5090 32GB
32 GBQ4_K_M3.0Too big
2× RX 7900 XTX 24GB
48 GBQ4_K_M3.0Too big
MacBook Pro M3 Max 64GB
64 GBQ4_K_M2.8Too big
MacBook Pro M1 Max 64GB
64 GBQ4_K_M2.6Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M2.5Too big
NVIDIA2× RTX 4090 24GB
48 GBQ4_K_M2.1Too big
NVIDIARTX 4090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M2.0Too big
NVIDIARTX 3090 24GB
24 GBQ4_K_M2.0Too big
NVIDIARTX 4070 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M2.0Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M2.0Too big
RX 7900 XTX 24GB
24 GBQ4_K_M2.0Too big
NVIDIA2× RTX 3090 24GB
48 GBQ4_K_M2.0Too big
NVIDIA4× RTX 3060 12GB
48 GBQ4_K_M2.0Too 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 Hy3 (295B params) fits at each quantization level on Mac Studio M3 Ultra 256GB (184.3 GB usable).

QuantBitsVRAMQualityFit
Q1_0_G128
1.125
42.5 GB
Very LowA81
Q2_0_G128
1.71
78.8 GB
LowS85
Q2_K
2
115.1 GB
LowS87
Q3_K_SBest for your GPU
3
144.6 GB
LowS87
NVFP4
4
165.2 GB
MediumF0
Q4_K_M
4
180.0 GB
MediumF0
Q5_K_M
5
212.4 GB
HighF0
Q6_K
6
241.9 GB
HighF0
Q8_0
8
315.7 GB
Very HighF0
F16
16
604.8 GB
MaximumF0

Get started

Copy-paste commands to run Hy3 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "tencent/Hy3" \ --hf-file "Hy3-Q4_K_M.gguf" \ -c 4096 -ngl 99

Frequently asked questions

Can Mac Studio M3 Ultra 256GB run Hy3?

Yes, Mac Studio M3 Ultra 256GB can run Hy3 with a A grade (Very compromised (needs ~24.5 GB host RAM)). Expected decode speed: 11.6 tok/s.

How much VRAM does Hy3 need?

Hy3 (295B parameters) requires approximately 213.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Hy3?

The recommended quantization for Hy3 is Q4_K_M, which balances quality and memory efficiency.

What speed will Hy3 run at on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Hy3 achieves approximately 11.6 tokens per second decode speed with a time-to-first-token of 16619ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 256GB run Hy3 for coding?

For coding workloads, Hy3 on Mac Studio M3 Ultra 256GB receives a A grade with 11.6 tok/s and 4K context.

What context window can Hy3 use on Mac Studio M3 Ultra 256GB?

On Mac Studio M3 Ultra 256GB, Hy3 can safely use up to 4K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Hy3 feels slow on Mac Studio M3 Ultra 256GB?

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.

Is unified memory on Mac Studio M3 Ultra 256GB as fast as VRAM for Hy3?

Not always. Mac Studio M3 Ultra 256GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M3 Ultra 256GBSee all hardware for Hy3
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