XLAM 1B Fc R — Hardware Requirements & GPU Compatibility
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XLAM 1B Fc R is a 1B-parameter open language model from Salesforce. It supports a context window of up to 16,384 tokens. At BF16 it needs about 2.70 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
59 likesSpecifications
- Publisher
- Salesforce
- Parameters
- 1B
- Architecture
- LlamaForCausalLM
- Context Length
- 16,384 tokens
- Vocabulary Size
- 32,256
- Release Date
- 2025-04-11
- License
- CC BY-NC 4.0
Get Started
HuggingFace
How Much VRAM Does XLAM 1B Fc R Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.7 GB | 5.5 GB | 2.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run XLAM 1B Fc R?
BF16 · 2.7 GBXLAM 1B Fc R (BF16) requires 2.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 16K context window can add up to 2.8 GB, bringing total usage to 5.5 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run XLAM 1B Fc R?
BF16 · 2.7 GB33 devices with unified memory can run XLAM 1B Fc R, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does XLAM 1B Fc R need?
XLAM 1B Fc R requires 2.7 GB of VRAM at BF16. Full 16K context adds up to 2.8 GB (5.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1B × 16 bits ÷ 8 = 2 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.5 GB (at full 16K context)
VRAM usage by quantization
BF162.7 GBBF16 + full context5.5 GB- Can I run XLAM 1B Fc R on a Mac?
XLAM 1B Fc R requires at least 2.7 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 XLAM 1B Fc R locally?
Yes — XLAM 1B Fc R can run locally on consumer hardware. At BF16 quantization it needs 2.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is XLAM 1B Fc R?
At BF16, XLAM 1B Fc R can reach ~1080 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~243 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 ÷ 2.7 × 0.55 = ~1080 tok/s
Estimated speed at BF16 (2.7 GB)
~1080 tok/s~243 tok/s~807 tok/s~668 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of XLAM 1B Fc R?
At BF16, the download is about 2.00 GB.
- Which GPUs can run XLAM 1B Fc R?
35 consumer GPUs can run XLAM 1B Fc R at BF16 (2.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run XLAM 1B Fc R?
33 devices with unified memory can run XLAM 1B Fc R at BF16 (2.7 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.