How to Run Llama 70B at Full FP16 Precision

Quantization is a compromise. Every time you convert a model from FP16 to INT8 or INT4, you're trading quality for the ability to fit the model in memory. For many applications, that trade-off is unacceptable.

This guide shows you how to run Llama 70B at full FP16 precision - no quantization, no quality loss - using MetalCloud's 512GB unified memory infrastructure.

Why Full Precision Matters

Quantization reduces model weights from 16-bit floats to 8-bit or 4-bit integers. This dramatically reduces memory requirements but introduces artifacts:

• Degraded reasoning: Complex multi-step reasoning suffers most from quantization noise
• Reduced accuracy: Mathematical and factual accuracy drops measurably
• Inconsistent outputs: The same prompt can produce different quality responses
• Lost nuance: Subtle distinctions in language and tone are flattened

For research, medical applications, financial analysis, and any use case where precision matters, full FP16 inference is essential.

Memory Requirements Breakdown

At INT4, Llama 70B fits on a single H100. But at full FP16, you need 200GB+ - that's 2-3 H100s with tensor parallelism, or a single Mac Studio on MetalCloud.

Step-by-Step: Running Llama 70B at Full Precision

Step 1: Install the MetalCloud SDK

pip install metalcloud

Step 2: Configure Your Job

import metalcloud

# Initialize with your API key
mc = metalcloud.Client(api_key="your-api-key")

# Create a job requesting 256GB+ memory for full FP16
job = mc.Job(
    model="meta-llama/Llama-3.3-70B-Instruct",
    precision="fp16",           # Full precision - no quantization
    min_memory_gb=256,         # Request enough for model + context
    max_price_per_hour=4.00    # Budget cap in GBP
)

Step 3: Run Inference

# Submit a prompt with a large context window
result = job.inference(
    prompt="""You are analyzing a complex legal document. 
    
[Insert your 50,000+ token document here]

Based on this document, provide a detailed analysis of the key risks and obligations.""",
    max_tokens=4000,
    temperature=0.7,
    context_window=65536      # 64K tokens - no problem with 512GB
)

print(result.text)

Step 4: Batch Processing (Optional)

# Process multiple prompts efficiently
prompts = [
    "Analyze this financial report...",
    "Review this research paper...",
    "Summarize this legal contract...",
]

results = job.batch_inference(
    prompts=prompts,
    max_tokens=2000,
    parallel=True             # Process in parallel when possible
)

for i, result in enumerate(results):
    print(f"Result {i+1}: {result.text[:200]}...")

Performance Expectations

Running at full FP16 on Apple Silicon, expect these approximate performance numbers:

While raw token throughput is lower than quantized models on H100s, you're getting full precision quality that's simply impossible to achieve on single-GPU setups.

When to Use Full Precision

Full FP16 inference is ideal for:

• Research and benchmarking: When you need reproducible, maximum-quality outputs
• Medical and legal applications: Where accuracy is non-negotiable
• Financial analysis: Complex reasoning about numbers and trends
• Creative writing: Preserving nuance and stylistic quality
• Quality baseline: Establishing ground truth before testing quantized versions

For high-throughput applications where slight quality degradation is acceptable, INT8 or INT4 quantization on traditional GPU infrastructure may be more cost-effective. But when quality is the priority, full precision on MetalCloud is the only practical option for single-machine deployment.

Cost Comparison