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
- 2025-03-27
- License
- CC BY-NC 4.0
Get Started
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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.0 GB | 20.9 GB | 3.40 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.5 GB | 21.4 GB | 3.90 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.4 GB | 22.3 GB | 4.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.3 GB | 23.2 GB | 5.70 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.2 GB | 24.1 GB | 6.60 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.6 GB | 25.5 GB | 8.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 16.6 GB | 33.5 GB | 16.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama XLAM 2 8B Fc R?
Q4_K_M · 5.4 GBLlama XLAM 2 8B Fc R (Q4_K_M) requires 5.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 22.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama XLAM 2 8B Fc R?
Q4_K_M · 5.4 GB58 devices with unified memory can run Llama XLAM 2 8B Fc R, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llama XLAM 2 8B Fc R need?
Llama XLAM 2 8B Fc R requires 5.4 GB of VRAM at Q4_K_M, or 16.6 GB at BF16. Full 131K context adds up to 16.9 GB (22.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 4.8 bits ÷ 8 = 4.8 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
Q4_K_M5.4 GBQ4_K_M + full context22.3 GB- What's the best quantization for Llama XLAM 2 8B Fc R?
For Llama XLAM 2 8B Fc R, Q4_K_M (5.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.0 GB.
VRAM requirement by quantization
Q2_K4.0 GBQ4_K_M ★5.4 GBQ5_K_M6.3 GBQ6_K7.2 GBQ8_08.6 GBBF1616.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama XLAM 2 8B Fc R on a Mac?
Llama XLAM 2 8B Fc R requires at least 4.0 GB at Q2_K, 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 Q4_K_M quantization it needs 5.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama XLAM 2 8B Fc R?
At Q4_K_M, Llama XLAM 2 8B Fc R can reach ~819 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~122 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 5.4 × 0.65 = ~968 tok/s
Estimated speed at Q4_K_M (5.4 GB)
~968 tok/s~122 tok/s~968 tok/s~819 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 Q4_K_M, the download is about 4.80 GB. The full-precision BF16 version is 16.00 GB. The smallest option (Q2_K) is 3.40 GB.
- Which GPUs can run Llama XLAM 2 8B Fc R?
50 consumer GPUs can run Llama XLAM 2 8B Fc R at Q4_K_M (5.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama XLAM 2 8B Fc R?
59 devices with unified memory can run Llama XLAM 2 8B Fc R at Q4_K_M (5.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.