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Synopsis

llmfit recommend [OPTIONS]

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Description

Provides curated recommendations of the best models for your system based on fit quality, use case, runtime, and other filters. This is optimized for programmatic use and defaults to JSON output.

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Options

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-n, --limit
integer
default:"5"
Number of recommendations to return.
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--use-case
enum
Filter by use case category. Options:
  • general - General-purpose models
  • coding (alias: code) - Code generation and analysis
  • reasoning (alias: reason) - Complex reasoning tasks
  • chat - Conversational models
  • multimodal (alias: vision) - Vision and multimodal
  • embedding (alias: embed) - Text embeddings
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--min-fit
enum
default:"marginal"
Filter by minimum fit level. Options:
  • perfect - Only perfect fits
  • good - Good or better
  • marginal - Marginal or better (default)
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--runtime
enum
default:"any"
Filter by inference runtime. Options:
  • any - All runtimes (default)
  • mlx - MLX only (Apple Silicon)
  • llamacpp (aliases: llama.cpp, llama_cpp) - llama.cpp only
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--json
boolean
default:"true"
Output as JSON. Default is true for this command.
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--memory
string
Override GPU VRAM size (e.g., “32G”, “32000M”, “1.5T”).
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--max-context
integer
Cap context length used for memory estimation (tokens). Must be >= 1.

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Usage Examples

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Basic Recommendations

# Get top 5 recommendations (default)
llmfit recommend

# Get top 10 recommendations
llmfit recommend -n 10

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Filter by Use Case

# Best coding models
llmfit recommend --use-case coding -n 3

# Best reasoning models
llmfit recommend --use-case reasoning -n 5

# Best chat models
llmfit recommend --use-case chat

# Multimodal models
llmfit recommend --use-case multimodal

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Filter by Fit Level

# Only perfect fits
llmfit recommend --min-fit perfect -n 10

# Good or better
llmfit recommend --min-fit good

# Include marginal fits (default)
llmfit recommend --min-fit marginal

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Filter by Runtime

# MLX models only (Apple Silicon)
llmfit recommend --runtime mlx -n 5

# llama.cpp models only
llmfit recommend --runtime llamacpp -n 5

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Combined Filters

# Top 3 perfect-fit coding models
llmfit recommend --use-case coding --min-fit perfect -n 3

# Top 5 good MLX models
llmfit recommend --runtime mlx --min-fit good -n 5

# Best reasoning models for 24GB VRAM
llmfit recommend --use-case reasoning --memory 24G

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Human-Readable Output

# Disable JSON output
llmfit recommend --json false -n 10

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Example Output

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JSON Format (Default)

$ llmfit recommend -n 3

{
  "system": {
    "total_ram_gb": 64.0,
    "available_ram_gb": 58.24,
    "cpu_cores": 16,
    "cpu_name": "Apple M2 Max",
    "has_gpu": true,
    "gpu_vram_gb": 64.0,
    "unified_memory": true,
    "backend": "Metal"
  },
  "models": [
    {
      "name": "llama-3.3-70b",
      "provider": "Meta",
      "parameter_count": "70B",
      "params_b": 70.0,
      "context_length": 131072,
      "use_case": "general",
      "category": "General",
      "release_date": "2024-12-06",
      "fit_level": "perfect",
      "run_mode": "gpu",
      "score": 95.2,
      "estimated_tps": 42.5,
      "runtime": "MLX",
      "best_quant": "4bit",
      "memory_required_gb": 43.68,
      "utilization_pct": 68.2
    },
    {
      "name": "qwen-2.5-72b",
      "provider": "Alibaba",
      "parameter_count": "72B",
      "params_b": 72.0,
      "context_length": 32768,
      "use_case": "general",
      "category": "General",
      "release_date": "2024-09-19",
      "fit_level": "perfect",
      "run_mode": "gpu",
      "score": 94.8,
      "estimated_tps": 40.1,
      "runtime": "MLX",
      "best_quant": "4bit",
      "memory_required_gb": 45.79,
      "utilization_pct": 71.5
    },
    {
      "name": "deepseek-v3",
      "provider": "DeepSeek",
      "parameter_count": "671B",
      "params_b": 671.0,
      "context_length": 131072,
      "use_case": "reasoning",
      "category": "Reasoning",
      "release_date": "2024-12-26",
      "fit_level": "good",
      "run_mode": "moe_offload",
      "score": 92.1,
      "estimated_tps": 28.3,
      "runtime": "llamacpp",
      "best_quant": "Q4_K_M",
      "memory_required_gb": 57.42,
      "utilization_pct": 89.7
    }
  ]
}

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Filtered by Use Case

$ llmfit recommend --use-case coding -n 3

{
  "system": { ... },
  "models": [
    {
      "name": "qwen-2.5-coder-32b",
      "provider": "Alibaba",
      "parameter_count": "32B",
      "use_case": "coding",
      "category": "Coding",
      "fit_level": "perfect",
      "score": 91.2,
      "estimated_tps": 68.2
    },
    {
      "name": "codestral-25.01",
      "provider": "Mistral",
      "parameter_count": "22B",
      "use_case": "coding",
      "category": "Coding",
      "fit_level": "perfect",
      "score": 89.5,
      "estimated_tps": 85.1
    },
    {
      "name": "deepseek-coder-v2",
      "provider": "DeepSeek",
      "parameter_count": "236B",
      "use_case": "coding",
      "category": "Coding",
      "fit_level": "good",
      "score": 87.3,
      "estimated_tps": 32.7
    }
  ]
}

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Human-Readable Format

$ llmfit recommend --json false -n 5

╭─ System Hardware ──────────────────────────────────────────╮
│  RAM:  64.0 GB total (58.2 GB available)                  │
│  CPU:  16 cores (Apple M2 Max)                            │
│  GPU:  Metal - Apple M2 Max (64.0 GB, unified memory)     │
╰────────────────────────────────────────────────────────────╯

╭─────────────┬──────────────────────┬───────────┬──────┬───────┬──────────────┬─────────┬────────────┬─────────┬────────┬─────────╮
│ Status      │ Model                │ Provider  │ Size │ Score │ tok/s est.   │ Quant   │ Runtime    │ Mode    │ Mem %  │ Context │
├─────────────┼──────────────────────┼───────────┼──────┼───────┼──────────────┼─────────┼────────────┼─────────┼────────┼─────────┤
│ ✓ Perfect   │ llama-3.3-70b        │ Meta      │ 70B  │ 95    │ 42.5         │ 4bit    │ MLX        │ GPU     │ 68.2%  │ 128k    │
│ ✓ Perfect   │ qwen-2.5-72b         │ Alibaba   │ 72B  │ 95    │ 40.1         │ 4bit    │ MLX        │ GPU     │ 71.5%  │ 32k     │
│ ✓ Good      │ deepseek-v3          │ DeepSeek  │ 671B │ 92    │ 28.3         │ Q4_K_M  │ llama.cpp  │ GPU     │ 89.7%  │ 128k    │
│ ✓ Perfect   │ qwen-2.5-coder-32b   │ Alibaba   │ 32B  │ 91    │ 68.2         │ 4bit    │ MLX        │ GPU     │ 52.3%  │ 128k    │
│ ✓ Perfect   │ llama-3.1-70b        │ Meta      │ 70B  │ 91    │ 42.5         │ 4bit    │ MLX        │ GPU     │ 68.2%  │ 128k    │
╰─────────────┴──────────────────────┴───────────┴──────┴───────┴──────────────┴─────────┴────────────┴─────────┴────────┴─────────╯

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Filtering Behavior

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Backend Compatibility

  • MLX-only models are automatically hidden on non-Apple Silicon systems
  • CUDA/ROCm models require appropriate GPU drivers

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Fit Level Filtering

  • --min-fit perfect: Only models with Perfect fit level
  • --min-fit good: Perfect and Good fit levels
  • --min-fit marginal: Perfect, Good, and Marginal (default)
  • Too Tight models are excluded by default

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Use Case Categories

  • General: Versatile models for various tasks
  • Coding: Code generation, analysis, completion
  • Reasoning: Complex logical and mathematical reasoning
  • Chat: Conversational and instruction-following
  • Multimodal: Vision and image understanding
  • Embedding: Text embedding generation

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Use Cases

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CI/CD Integration

#!/bin/bash
# Select best model for deployment
BEST_MODEL=$(llmfit recommend -n 1 --min-fit perfect | jq -r '.models[0].name')
echo "Deploying $BEST_MODEL"

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Auto-Configuration

# Get best coding model for development environment
CODER=$(llmfit recommend --use-case coding -n 1 | jq -r '.models[0].name')
ollama pull $CODER

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Hardware Selection

# Test different VRAM configurations
for vram in 24 32 48 64 80; do
  echo "== ${vram}GB VRAM =="
  llmfit recommend --memory "${vram}G" --min-fit perfect -n 3 | jq '.models[].name'
done
  • fit - See all compatible models
  • info - Get detailed model information
  • system - Verify detected hardware
  • search - Search for specific models

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