Recipe
qwen3-5-35b-a3b-base-nvfp4-apple-m5-max-128gb-ollama-tp1
qwen3-5-35b-a3b-base-nvfp4-apple-m5-max-128gb-ollama-tp1Observed LocalMaxxing leaderboard run. Evidence for compatibility, not an executable launch contract.
Record
- Status
- candidate
- Source
- localmaxxing
- Engine
- ollama
- Accelerators
- 1
- Tensor parallel
- 1
- Context tokens
- 262,144
- Max concurrency
- 1
- chat
- unknown
- reasoning
- unknown
- tools
- unknown
- vision
- unknown
Hugging Face model card
Identity
https://huggingface.co/Qwen/Qwen3.5-35B-A3B- Repository
- Qwen/Qwen3.5-35B-A3B
- Status
- known
- Link type
- Exact Hub repository
Public Hugging Face repository confirmed by the Hub API.
Candidate: useful compatibility or speed evidence. The registry does not offer Run until promotion requirements are met.
Observed configuration
ollama
Evidence only · candidate · reference
Candidate evidence — not a Run contract
Observed source tokens
Mechanical split of the source command. Unverified against the engine CLI. The registry does not offer Run for this recipe.
ollamarun--verbose--thinkfalse--hidethinkingExplain the concept of attention mechanisms in transformer models. Include the key components: query, key, value pairs. Discuss how scaled dot-product attention works. Provide a detailed Python code example. Aim for at least 300 words.
| Flag | Value |
|---|---|
--think | false |
--hidethinking | Explain the concept of attention mechanisms in transformer models. Include the key components: query, key, value pairs. Discuss how scaled dot-product attention works. Provide a detailed Python code example. Aim for at least 300 words. |
Measured speed
| Concurrency | Context | Prefill | Decode | TTFT ms | Status | Sweep |
|---|---|---|---|---|---|---|
| 1 | 262,144 | — | 122 | — | observed | qwen3-5-35b-a3b-base-nvfp4-apple-m5-max-128gb-ollama-tp1-sweep |
Remaining fields
Identity, launch, related records, and measured speed are shown above. This is the rest of the normalized record.
- hardware count
- 1
- hardware id
- apple-m5-max-128gb
- id
- qwen3-5-35b-a3b-base-nvfp4-apple-m5-max-128gb-ollama-tp1
- model instance id
- qwen-qwen3-5-35b-a3b--nvfp4
- recipe source
- localmaxxing
- schema version
- local-ai-registry/v1
- speed sweep ids
- qwen3-5-35b-a3b-base-nvfp4-apple-m5-max-128gb-ollama-tp1-sweep
- status
- candidate
capabilities
engine
- name
- ollama
serving
- max concurrency
- 1
- max context tokens
- 262,144
- tensor parallel
- 1
Provenance & metadata (3)
facts
- capabilities.chat · provenance · captured at
- 2026-08-30T09:10:02Z
capabilities.chat · reason capability-not-verified
capabilities.chat · state unknown
- capabilities.reasoning · provenance · captured at
- 2026-08-30T09:10:02Z
capabilities.reasoning · reason capability-not-verified
capabilities.reasoning · state unknown
- capabilities.tools · provenance · captured at
- 2026-08-30T09:10:02Z
capabilities.tools · reason capability-not-verified
capabilities.tools · state unknown
- capabilities.vision · provenance · captured at
- 2026-08-30T09:10:02Z
capabilities.vision · reason capability-not-verified
capabilities.vision · state unknown
- engine.graph mode · provenance · captured at
- 2026-08-30T09:10:02Z
engine.graph mode · reason runtime-detail-not-published
engine.graph mode · state unknown
- serving.kv cache tokens · provenance · captured at
- 2026-08-30T09:10:02Z
serving.kv cache tokens · reason kv-cache-capacity-not-published
serving.kv cache tokens · state unknown
metadata
- localmaxxing · backend
- metal
- localmaxxing · hardware label
- M5 Max
- localmaxxing · notes
- total duration: 20.85862475s load duration: 20.927791ms prompt eval count: 60 token(s) prompt eval duration: 149.81525ms prompt eval rate: 400.49 tokens/s eval count: 2524 token(s) eval duration: 20.6869895s eval rate: 122.01 tokens/s
- localmaxxing · observed command
- ollama run --verbose --think false --hidethinking "Explain the concept of attention mechanisms in transformer models. Include the key components: query, key, value pairs. Discuss how scaled dot-product attention works. Provide a detailed Python code example. Aim for at least 300 words."
- localmaxxing · run id
- cmoj9uhlc0007jl04y8ygqwap
- localmaxxing · tokenized · arguments
- ollama, run, --verbose, --think, false, --hidethinking, Explain the concept of attention mechanisms in transformer models. Include the key components: query, key, value pairs. Discuss how scaled dot-product attention works. Provide a detailed Python code example. Aim for at least 300 words.
- localmaxxing · tokenized · fidelity
- faithful
provenance
- captured at
- 2026-08-30T09:10:02Z
sources
| captured at | kind | url |
|---|---|---|
| 2026-08-30T09:10:02Z | normalized-recipe | www.localmaxxing.com/en/runs/cmoj9uhlc0007jl04y8ygqwap ↗ |