Can Hy3 run on GTX 1650 4GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

Hy3 needs ~186.1 GB but GTX 1650 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 186.1 GB, exceeds 4.0 GB available

186.1 GB required4.0 GB available

4653% VRAM needed

182.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

96800 ms

Safe context

Memory

186.1 GB / 4.0 GB

Offload

100%

Memory breakdown

Weights180.0 GB

KV Cache4.9 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsHy3 on GTX 1650 4GB
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: 2.0 tok/s decode · 96.8s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 186.1 GB, but this setup only exposes 4.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	2.0 tok/s	52800 ms	4K
Coding	F	Too heavy	2.0 tok/s	96800 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	114400 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	11.6	Heavy offload

Quantization options

How Hy3 (295B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	42.5 GB	Very Low	F0
Q2_0_G128	1.71	78.8 GB	Low	F0
Q2_K	2

Upgrade options

Hardware that runs Hy3 well

AMD Instinct MI350X 288GBBiggest leap

288 GB VRAM (+284)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.98.9 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$8,000 MSRP

NVIDIA B200 180GBBest value

180 GB VRAM (+176)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.79.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 3855%.

~$30,000 MSRP

B100 192GBBudget pick

192 GB VRAM (+188)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.87.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 4255%.

~$35,000 MSRP

NVIDIA GB200 192GBNVIDIA upgrade

192 GB VRAM (+188)8000 GB/s (+7872)

Makes the model fit on the accelerator instead of staying completely out of reach.87.1 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 4255%.

~$60,000 MSRP

Frequently asked questions

Can GTX 1650 4GB run Hy3?

No, Hy3 requires more memory than GTX 1650 4GB provides.

How much VRAM does Hy3 need?

Hy3 (295B parameters) requires approximately 186.1 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 GTX 1650 4GB?

On GTX 1650 4GB, Hy3 achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.

Can GTX 1650 4GB run Hy3 for coding?

For coding workloads, Hy3 on GTX 1650 4GB receives a F grade with 2.0 tok/s and 4K context.

What context window can Hy3 use on GTX 1650 4GB?

On GTX 1650 4GB, 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 GTX 1650 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1650 4GB See all hardware for Hy3

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