Recursive Flint runs at 80 tok/s (79.57 as recorded) on a 48 GB M4 Pro, a community run reported by github.com (llama.cpp, Q4_1, 512-token coding answers, median of 5 at T 1.0). It needs about 20 GB of unified memory. It is Recursive's 39.8B mixture-of-experts model (2.5B active), released under the Apache 2.0 licence.
Want to run Recursive Flint faster — or step up to bigger models? Find your Mac →This is a community run: a real run, reported by its source and linked below. The index records it as given and never adjusts it.
| Metric | Value |
|---|---|
| Tokens per second | community80 tok/s |
| Provenance | community run · github.com |
| Quantization | Q4_1 |
| Engine | llama.cpp |
| Context | 512-token coding answers, median of 5 at T 1.0 |
| Figure dated | 2026-10-22 |
| Minimum RAM | 20 GB |
| Parameters | 39.8B MoE (2.5B active) |
| Chip bandwidth | 273 GB/s |
Each row as the index holds it, with its source. “Mac RAM” is the memory of the Mac a run used, not the model’s requirement (20 GB). The headline figure follows the reference rule: the lower-median measured plain-decode run at 5 bits or fewer (a run whose quant was not stated counts only when no stated-quant run qualifies), else the estimate; a run marked “not used to anchor estimates” is listed here but never becomes the headline.
| Speed | Quant | Engine | Mac RAM | Context | Source | Note |
|---|---|---|---|---|---|---|
| sourced131.2 tok/s | INT4 | rec | 48 GB | 1K prompt, 1,024 generated, 5 runs | github.com | Recursive's own Metal engine, rec 0.1.0 (a vendor runtime, so it does not anchor), on RecursiveSI/flint-int4 (MLX group-64 INT4, INT8 embeddings); prefill excluded. Read from the throughput chart's description in the rec README; the source of Recursive's '>125 tok/s on an M4 Pro' claim. 4.54 bpw = the 22.6 GB RecursiveSI/flint-int4 checkpoint over 39.8B parameters.; not used to anchor estimates |
| sourced88 tok/s | 4-bit | llama.cpp | 48 GB | 1K prompt, 1,024 generated, 5 runs | github.com | Recursive's llama.cpp baseline on the same chart; build not stated and the chart was committed before PR #30928 merged, so it does not anchor; GGUF type not stated.; not used to anchor estimates |
| community79.57 tok/s | Q4_1 | llama.cpp | 48 GB | 512-token coding answers, median of 5 at T 1.0 | github.com | MacBook Pro, 20-core GPU. llama.cpp b11804 (its first build with Flint), Recursive's Q4_1 GGUF (RecursiveSI/flint-int4-gguf), -b/-ub 2048; greedy 79.93. Light foreign-GPU contention flagged (3.85% mean). Run by the Kapitsa engine's developers with their own harness. |
Ollama — install it (brew install ollama or the app from ollama.com), then pull the model by its tag, checked on ollama.com on 2026-10-24:
MLX — pick a build from Hugging Face’s MLX listings, then run it with its repo id:
Downloads: Ollama page → · model card →
The 80 tok/s figure above came from llama.cpp at Q4_1; other runtimes and quants run at different speeds.
Prefer a GUI? LM Studio downloads GGUF and MLX builds in one click. Step by step: install Ollama on a Mac.
Recursive's Apache 2.0 coding-agent MoE (21 Oct 2026): 39.8B total, ~2.5B active, 262K context, text only, always reasons. Its card reports SWE-bench Verified 74.2 and Terminal-Bench 2.1 54.9 (its SWE-bench Pro v2 89.7 is an internal mini-swe-agent run, not comparable to SEAL's); the index's capability figure is estimated from these vendor coding benchmarks only. Stock llama.cpp b11803+, Ollama (flint:40b) and LM Studio run it, though llama.cpp's expert-scaling workaround costs 10–30% for now and issue #31000 (NVFP4 tool calls) is open. A ~18 GB 3-bit build fits a 24 GB Mac; community llama.cpp runs measured 92.4 tok/s on an M5 Pro and 79.6 on an M4 Pro (Q4_1).
Apache 2.0 licence · 39.8B MoE · 262K context · LLMCheck capability 37/50 · see its leaderboard rank →
Recursive Flint needs about 20 GB of unified memory. These current Apple Silicon Macs have the headroom to run it comfortably:
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