MarvinLabs vram_fit
Will a QLoRA fine-tune of gemma-3-4b-it fit your GPU? Verdicts from measured AMD and NVIDIA probes.
Sollte ich dies verwenden
Qualität und Sicherheit
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"vram-fit": {
"url": "https://marvinlabs.org/mcp"
}
}
}Remote-Endpunkte
https://marvinlabs.org/mcpstreamable-httpWas es kann
Tool-Inventar
Tools (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.
Eingabe-Schema
{
"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).
Eingabe-Schema
{
"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'.
Eingabe-Schema
{
"type": "object",
"properties": {
"rank": {
"type": "integer"
},
"seq_len": {
"type": "integer"
},
"quant": {
"type": "string"
},
"lora_dropout": {
"type": "number"
},
"dora": {
"type": "boolean"
},
"gpu": {
"type": "string"
}
}
}Community
Nachweis