Can DiffusionGemma 26B A4B run on RTX 4070 12GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

DiffusionGemma 26B A4B needs ~23.0 GB but RTX 4070 12GB only has 12.0 GB. Try a smaller quantization or lighter model.

Runtime: vLLMCapacity: No fitBandwidth: MediumStack: OptimizedBottleneck: 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) — 23.0 GB, exceeds 12.0 GB available

23.0 GB required12.0 GB available

192% VRAM needed

11.0 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.6 tok/s

TTFT

22463 ms

Safe context

Memory

23.0 GB / 12.0 GB

Offload

50%

Memory breakdown

Weights15.7 GB

KV Cache3.7 GB

Runtime2.4 GB

Headroom1.2 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDiffusionGemma 26B A4B on RTX 4070 12GB
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: 8.6 tok/s decode · 22.5s TTFT (warm) · 22 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 23.0 GB, but this setup only exposes 12.0 GB of usable VRAM.

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	10.3 tok/s	10289 ms	4K
Coding	F	Too heavy	8.6 tok/s	22463 ms	4K
Agentic Coding	F	Too heavy	6.8 tok/s	41533 ms	4K
Reasoning	F	Too heavy	8.6 tok/s	26547 ms	4K
RAG	F	Too heavy	6.8 tok/s	51916 ms	4K

Inference speed

DiffusionGemma 26B A4B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for DiffusionGemma 26B A4B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 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?
RTX 5090 32GB	32 GB	Q4_K_M	143.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	67.5	Too big
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	66.6	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	60.9	Too big
RTX 3090 24GB	24 GB	Q4_K_M	57.7	Too big
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	52.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	41.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	41.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	26.3	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	25.1	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	24.6	Too big
RTX 4070 12GB	12 GB	Q4_K_M	8.6	Too big
RTX 3060 12GB	12 GB	Q4_K_M	5.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.6	Too 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 DiffusionGemma 26B A4B (25.799999237060547B params) fits at each quantization level on RTX 4070 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.1 GB	Low	F0
Q3_K_S	3	12.6 GB	Low	F0
NVFP4	4	14.4 GB	Medium	F0
Q4_K_M	4	15.7 GB	Medium	F0
Q5_K_M	5	18.6 GB	High	F0
Q6_K	6	21.2 GB	High	F0
Q8_0	8	27.6 GB	Very High	F0
F16	16	52.9 GB	Maximum	F0

升级选项

能流畅运行 DiffusionGemma 26B A4B 的硬件

RTX 5090 32GB经济之选

32 GB VRAM (+20)1792 GB/s (+1288)

Makes the model fit on the accelerator instead of staying completely out of reach.143.6 tok/s 解码

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.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GB最佳性价比

32 GB VRAM (+20)896 GB/s (+392)

Makes the model fit on the accelerator instead of staying completely out of reach.90 tok/s 解码

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.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA 升级

32 GB VRAM (+20)576 GB/s (+72)

Makes the model fit on the accelerator instead of staying completely out of reach.55.1 tok/s 解码

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.

~$4,000 MSRP

Frequently asked questions

Can RTX 4070 12GB run DiffusionGemma 26B A4B?

No, DiffusionGemma 26B A4B requires more memory than RTX 4070 12GB provides.

How much VRAM does DiffusionGemma 26B A4B need?

DiffusionGemma 26B A4B (25.799999237060547B parameters) requires approximately 23.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DiffusionGemma 26B A4B?

The recommended quantization for DiffusionGemma 26B A4B is Q4_K_M, which balances quality and memory efficiency.

What speed will DiffusionGemma 26B A4B run at on RTX 4070 12GB?

On RTX 4070 12GB, DiffusionGemma 26B A4B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22463ms using Q4_K_M quantization.

Can RTX 4070 12GB run DiffusionGemma 26B A4B for coding?

For coding workloads, DiffusionGemma 26B A4B on RTX 4070 12GB receives a F grade with 8.6 tok/s and 4K context.

What context window can DiffusionGemma 26B A4B use on RTX 4070 12GB?

On RTX 4070 12GB, DiffusionGemma 26B A4B 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 DiffusionGemma 26B A4B feels slow on RTX 4070 12GB?

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 RTX 4070 12GB See all hardware for DiffusionGemma 26B A4B

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