Laguna XS 2.1 needs ~36.5 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~342 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
Fit status
Runs well
Decode
370.3 tok/s
TTFT
523 ms
Safe context
262K
Memory
34.7 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 341.9 tok/s | 350 ms | 262K |
| Coding | A | Runs well | 341.9 tok/s | 566 ms | 262K |
| Agentic Coding | A | Runs well | 341.9 tok/s | 824 ms | 262K |
| Reasoning | A | Runs well | 341.9 tok/s | 669 ms | 262K |
| RAG | A | Runs well | 341.9 tok/s | 1030 ms | 262K |
Inference speed
Estimated decode speed (tokens/sec) for Laguna XS 2.1 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? |
|---|---|---|---|---|
| 32 GB | Q4_K_M | 144.2 | Fits | |
| 24 GB | Q4_K_M | 85.8 | Offloads | |
How Laguna XS 2.1 (33.400001525878906B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q1_0_G128 | 1.125 | 4.8 GB | Very Low | B70 |
Q2_0_G128 | 1.71 | 8.9 GB | Low | A70 |
Q2_K | 2 |
Copy-paste commands to run Laguna XS 2.1 on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "poolside/Laguna-XS-2.1" \
--hf-file "Laguna-XS-2.1-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 123B | S | 37.5 tok/s | ||
| 122B | S |
Yes, Gaudi 3 128GB can run Laguna XS 2.1 with a A grade (Runs well). Expected decode speed: 341.9 tok/s.
Laguna XS 2.1 (33.400001525878906B parameters) requires approximately 36.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Laguna XS 2.1 is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Laguna XS 2.1 achieves approximately 341.9 tokens per second decode speed with a time-to-first-token of 566ms using Q4_K_M quantization.
For coding workloads, Laguna XS 2.1 on Gaudi 3 128GB receives a A grade with 341.9 tok/s and 262K context.
On Gaudi 3 128GB, Laguna XS 2.1 can safely use up to 262K tokens of context. 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/laguna-xs-2.1-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 256 GB |
| Q4_K_M |
| 79.6 |
| Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 72.3 | Offloads |
| 24 GB | Q4_K_M | 68.5 | Offloads |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 66.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 62.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 49.2 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 37.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 34.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 33.1 | Fits |
| 16 GB | Q4_K_M | 31.3 | Too big |
| 12 GB | Q4_K_M | 10.9 | Too big |
| 12 GB | Q4_K_M | 6.4 | Too big |
| 8 GB | Q4_K_M | 4.6 | 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.
13.0 GB |
| Low |
| A70 |
Q3_K_S | 3 | 16.4 GB | Low | A71 |
NVFP4 | 4 | 18.7 GB | Medium | A71 |
Q4_K_M | 4 | 20.4 GB | Medium | A71 |
Q5_K_M | 5 | 24.0 GB | High | A71 |
Q6_K | 6 | 27.4 GB | High | A72 |
Q8_0 | 8 | 35.7 GB | Very High | A73 |
F16Best for your GPU | 16 | 68.5 GB | Maximum | A79 |
| 104.1 tok/s |
| 35B | S | 329.1 tok/s |
| 111B | S | 41.8 tok/s |
| 72B | S | 64.1 tok/s |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.