FitMyLLM
Recursive/Mixture of Experts

RFlint

Flint — Recursive's first model, a 40B MoE for coding agents with about 2.5B active, trained from scratch. Most of its attention layers use a 513-token sliding window, which keeps the KV cache small; the full-precision checkpoint is about 80GB, so consumer hardware needs one of the 4-bit builds.

chatcodingreasoningagentictool_use
39.79B
Parameters (2.5B active)
256K
Context length
2
Benchmarks
14
Quantizations
33K
HF downloads
Architecture
MoE
Released
2026-10-21
Layers
49
KV Heads
4
Head Dim
128
Family
other

Quantization Options

Context length:
QuantBitsVRAM @ 16KQuality
IQ3_XXS3.25
17.1 GB
16.7 + 0.4 KV
low
IQ3_XS3.5
18.3 GB
17.9 + 0.4 KV
low
Q3_K_S3.64
19.0 GB
18.6 + 0.4 KV
low
IQ3_M3.76
19.6 GB
19.2 + 0.4 KV
low
Q3_K_M4
20.8 GB
20.4 + 0.4 KV
low
Q3_K_L4.3
22.3 GB
21.9 + 0.4 KV
moderate
IQ4_XS4.46
23.1 GB
22.7 + 0.4 KV
moderate
Q4_K_S4.67
24.1 GB
23.7 + 0.4 KV
moderate
Q4_K_M4.89
25.2 GB
24.8 + 0.4 KV
good
Q5_K_S5.57
28.6 GB
28.2 + 0.4 KV
good
Q5_K_M5.7
29.2 GB
28.8 + 0.4 KV
good
Q6_K6.56
33.5 GB
33.1 + 0.4 KV
excellent
Q8_08.5
43.2 GB
42.8 + 0.4 KV
lossless
FP1616
80.5 GB
80.1 + 0.4 KV
lossless

Select your GPU above to see speed estimates and compatibility for each quantization.

Deploying for a team or in production? Size GPUs, cost & scaling in Enterprise →
▸ READY TO RUN THIS?RENT BY THE HOUR

RENT A GPU AND RUN FLINT NOW

Spin up an A100 / H100 / 4090 in ~60s. Pay by the second. Cancel anytime.

Community Ratings

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Benchmarks (2)

LiveCodeBench80.3
SWE-bench74.2

Run this model

▸Easiest way to get started·Beginners
DOCS ↗
curl -fsSL https://ollama.com/install.sh | sh
$ollama run flint:40b

Tag may need adjustment — check ollama.com/library/flint for available tags.

▸ SETUP GUIDE
>_

Auto-setup with fitmyllm CLI

Detects your GPU, recommends the best model, downloads it, and starts chatting — zero config. Benchmarks your speed and contributes anonymous data to improve predictions.

pip install fitmyllmthen run fitmyllmLearn more
Auto-detect GPULive tok/s in chatSpeed benchmarks9 inference engines

GPUs that can run this model

At Q4_K_M quantization. Sorted by minimum VRAM.

Apple M1 Max (32GB)
32 GB VRAM • 400 GB/s
APPLE
$1499
Apple M2 Max (32GB)
32 GB VRAM • 400 GB/s
APPLE
$1799
NVIDIA V100 SXM2 32GB
32 GB VRAM • 900 GB/s
NVIDIA
$3500
Apple M2 Pro (32GB)
32 GB VRAM • 200 GB/s
APPLE
$1499
Apple M4 (32GB)
32 GB VRAM • 120 GB/s
APPLE
$1199
NVIDIA Tesla V100 DGXS 32 GB
32 GB VRAM • 897 GB/s
NVIDIA
NVIDIA Tesla V100 PCIe 32 GB
32 GB VRAM • 897 GB/s
NVIDIA
NVIDIA Tesla V100 SXM2 32 GB
32 GB VRAM • 898 GB/s
NVIDIA
NVIDIA Tesla V100 SXM3 32 GB
32 GB VRAM • 981 GB/s
NVIDIA
AMD Radeon Instinct MI60
32 GB VRAM • 1020 GB/s
AMD
NVIDIA Tesla V100S PCIe 32 GB
32 GB VRAM • 1130 GB/s
NVIDIA
AMD Radeon Instinct MI100
32 GB VRAM • 1230 GB/s
AMD
$5000
NVIDIA Tesla PG500-216
32 GB VRAM • 1130 GB/s
NVIDIA
NVIDIA Tesla PG503-216
32 GB VRAM • 1130 GB/s
NVIDIA

Find the best GPU for Flint

Build Hardware for Flint
▸ SPEC SHEET

Flint — 39.79B MoE.

▸ SPECIFICATIONS
PARAMETERS
39.79B (2.5B active)
ARCHITECTURE
Mixture of Experts
CONTEXT LENGTH
256K tokens
CAPABILITIES
chat, coding, reasoning, agentic, tool_use
RELEASE DATE
2026-10-21
PROVIDER
Recursive
FAMILY
other
▸ VRAM REQUIREMENTS
QUANTBPWVRAMQUALITY
IQ3_XXS3.2516.7 GB82%
IQ3_XS3.517.9 GB84%
Q3_K_S3.6418.6 GB85%
IQ3_M3.7619.2 GB86%
Q3_K_M420.4 GB88%
Q3_K_L4.321.9 GB90%
IQ4_XS4.4622.7 GB92%
Q4_K_S4.6723.7 GB93%
Q4_K_M4.8924.8 GB94%
Q5_K_S5.5728.2 GB96%
Q5_K_M5.728.8 GB96%
Q6_K6.5633.1 GB97%
Q8_08.542.8 GB100%
FP161680.1 GB100%
§ 01BENCHMARK SCORES
LiveCodeBench80.3
SWE-bench74.2
§ 02RUN COMMAND

Run Flint locally with Ollama — needs 24.8 GB VRAM at Q4_K_M:

$ollama run flint:40b
§ 03COMPATIBLE GPUs
30 @ Q4_K_M