DiffusionGemma 26B A4B needs ~21.1 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q3_K_S quantization, expect ~96 tok/s.
Operating mode
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.
Select quantization to explore
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
60.9 tok/s
TTFT
3178 ms
Safe context
15K
Memory
24.2 GB / 24.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 82.7 tok/s | 1278 ms | 15K |
| Coding | F | Too heavy | 60.9 tok/s | 3178 ms | 15K |
| Agentic Coding | F | Too heavy | 45.3 tok/s | 6219 ms | 15K |
| Reasoning | F | Too heavy | 60.9 tok/s | 3756 ms | 15K |
| RAG | F | Too heavy | 45.3 tok/s | 7774 ms | 15K |
Inference speed
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.
How DiffusionGemma 26B A4B (25.799999237060547B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.1 GB | Low | A78 |
Q3_K_S | 3 | 12.6 GB | Low | A79 |
NVFP4 | 4 | 14.4 GB | Medium | A79 |
Q4_K_M | 4 | 15.7 GB | Medium | A78 |
Q5_K_MBest for your GPU | 5 | 18.6 GB | High | A78 |
Q6_K | 6 | 21.2 GB | High | F0 |
Q8_0 | 8 | 27.6 GB | Very High | F0 |
F16 | 16 | 52.9 GB | Maximum | F0 |
Copy-paste commands to run DiffusionGemma 26B A4B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "google/diffusiongemma-26B-A4B-it" \
--hf-file "diffusiongemma-26B-A4B-it-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$2,249 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
Yes, RX 7900 XTX 24GB can run DiffusionGemma 26B A4B at Q3_K_S quantization (Tight fit). The recommended Q4_K_M requires 24.2 GB which exceeds available memory, but at Q3_K_S it needs only 21.1 GB. Expected decode speed: 95.7 tok/s.
DiffusionGemma 26B A4B (25.799999237060547B parameters) requires approximately 24.2 GB at Q4_K_M quantization. On RX 7900 XTX 24GB, it fits at Q3_K_S using 21.1 GB.
The recommended quantization is Q4_K_M, but on RX 7900 XTX 24GB the best fitting quantization is Q3_K_S, which uses 21.1 GB.
On RX 7900 XTX 24GB, DiffusionGemma 26B A4B achieves approximately 95.7 tokens per second decode speed with a time-to-first-token of 2023ms using Q3_K_S quantization.
For coding workloads, DiffusionGemma 26B A4B on RX 7900 XTX 24GB receives a F grade with 60.9 tok/s and 15K context.
On RX 7900 XTX 24GB, DiffusionGemma 26B A4B can safely use up to 29K tokens of context at Q3_K_S quantization. The model's official context limit is 262K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/diffusiongemma-26b-a4b-on-rx-7900-xtx-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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