Llama XLAM 2 8B Fc R — Hardware Requirements & GPU Compatibility
ChatFunctions
xLAM 2 8B FC-R is an 8-billion parameter model by Salesforce, specifically optimized for function calling and tool use. Built on the Llama architecture, it is designed to reliably generate structured function call outputs, making it suitable for agentic workflows and applications that require models to interact with external tools and APIs. Unlike general-purpose chat models, xLAM 2 focuses on accurately parsing user intent into structured tool invocations with proper argument formatting. It runs on consumer GPUs with 8GB or more of VRAM and is a strong choice for developers building local AI agent systems that need reliable function-calling capabilities.
64.1K downloads
59 likesSpecifications
- Publisher
- Salesforce
- Family
- Llama
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-05-06
- License
- CC BY-NC 4.0
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HuggingFace
How Much VRAM Does Llama XLAM 2 8B Fc R Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 33.5 GB | 16.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama XLAM 2 8B Fc R?
BF16 · 16.6 GBLlama XLAM 2 8B Fc R (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 33.5 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llama XLAM 2 8B Fc R?
BF16 · 16.6 GB21 devices with unified memory can run Llama XLAM 2 8B Fc R, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does Llama XLAM 2 8B Fc R need?
Llama XLAM 2 8B Fc R requires 16.6 GB of VRAM at BF16. Full 131K context adds up to 16.9 GB (33.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 16 bits ÷ 8 = 16 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context33.5 GB- Can I run Llama XLAM 2 8B Fc R on a Mac?
Llama XLAM 2 8B Fc R requires at least 16.6 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama XLAM 2 8B Fc R locally?
Yes — Llama XLAM 2 8B Fc R can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama XLAM 2 8B Fc R?
At BF16, Llama XLAM 2 8B Fc R can reach ~176 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~40 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 16.6 × 0.55 = ~176 tok/s
Estimated speed at BF16 (16.6 GB)
AMD Instinct MI300X~176 tok/sNVIDIA GeForce RTX 4090~40 tok/sNVIDIA H100 SXM~132 tok/sAMD Instinct MI250X~109 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama XLAM 2 8B Fc R?
At BF16, the download is about 16.00 GB.