MarvinLabs vram_fit
Will a QLoRA fine-tune of gemma-3-4b-it fit your GPU? Verdicts from measured AMD and NVIDIA probes.
¿Debería usar esto?
Calidad y seguridad
Basado en el análisis automatizado de las definiciones de herramientas y el cumplimiento del protocolo.
Costo de contexto
Este es el número aproximado de tokens que se consumen cada vez que las herramientas del servidor se cargan en el contexto de un modelo. Los recuentos más altos reducen la atención disponible para otras tareas.
Instalar
Instalación con un clic
Agrega esto a tu archivo `claude_desktop_config.json`:
{
"mcpServers": {
"vram-fit": {
"url": "https://marvinlabs.org/mcp"
}
}
}Puntos de conexión remotos
https://marvinlabs.org/mcpstreamable-httpQué puede hacer
Inventario de herramientas
Herramientas (3)
🟢vram_fit(vram_gb, rank, seq_len, response_len, quant, ...)
Will this LoRA config train on a card with vram_gb GB? Returns verdict fits / tight / does_not_fit, the predicted peak in MB, the margin, how each part was derived, and any measured probe with the same settings.
Esquema de entrada
{
"type": "object",
"properties": {
"vram_gb": {
"type": "number",
"description": "Card memory as sold, in GB (8, 12, 16, 24...)"
},
"rank": {
"type": "integer",
"minimum": 1
},
"seq_len": {
"type": "integer",
"default": 7177,
"description": "Longest training example, in tokens"
},
"response_len": {
"type": "integer",
"default": 237,
"description": "Tokens the loss covers (the response span)"
},
"quant": {
"type": "string",
"enum": [
"4bit",
"bf16"
],
"default": "4bit",
"description": "Base weights: bitsandbytes nf4 or bf16"
},
"lora_dropout": {
"type": "number",
"default": 0
},
"dora": {
"type": "boolean",
"default": false
},
"other_processes_mb": {
"type": "number",
"default": 0,
"description": "VRAM already held by other programs"
},
"model": {
"type": "string",
"default": "unsloth/gemma-3-4b-it"
},
"os": {
"type": "string",
"default": "linux",
"description": "windows or linux; Windows spills instead of OOM"
}
},
"required": [
"vram_gb",
"rank"
]
}🟢vram_max_rank(vram_gb, seq_len, response_len, quant, lora_dropout, ...)
Largest LoRA rank (multiple of 8) that fits with margin on a card with vram_gb GB, and the largest that is tight (a real run may fit, the one-step probe would not).
Esquema de entrada
{
"type": "object",
"properties": {
"vram_gb": {
"type": "number",
"description": "Card memory as sold, in GB (8, 12, 16, 24...)"
},
"seq_len": {
"type": "integer",
"default": 7177,
"description": "Longest training example, in tokens"
},
"response_len": {
"type": "integer",
"default": 237,
"description": "Tokens the loss covers (the response span)"
},
"quant": {
"type": "string",
"enum": [
"4bit",
"bf16"
],
"default": "4bit",
"description": "Base weights: bitsandbytes nf4 or bf16"
},
"lora_dropout": {
"type": "number",
"default": 0
},
"dora": {
"type": "boolean",
"default": false
},
"other_processes_mb": {
"type": "number",
"default": 0,
"description": "VRAM already held by other programs"
},
"model": {
"type": "string",
"default": "unsloth/gemma-3-4b-it"
}
},
"required": [
"vram_gb"
]
}🟢vram_probes(rank, seq_len, quant, lora_dropout, dora, ...)
The raw measured probe rows (RX 7900 XTX and RTX 3060 Ti), filtered by any of the given settings. gpu matches part of the card name, e.g. '7900' or '3060'.
Esquema de entrada
{
"type": "object",
"properties": {
"rank": {
"type": "integer"
},
"seq_len": {
"type": "integer"
},
"quant": {
"type": "string"
},
"lora_dropout": {
"type": "number"
},
"dora": {
"type": "boolean"
},
"gpu": {
"type": "string"
}
}
}Comunidad
Evidencia