Splitting LLMs Across GPUs

1. Introduction: The Memory Wall Problem

“Running Llama 3 70B? You’ll need 140GB+ VRAM – but no single GPU has that… yet.” This harsh reality stops many AI teams in their tracks. Modern LLMs like the 400B-parameter giants require more memory than even NVIDIA’s flagship H200 GPU (141GB) can provide. As models grow larger and contexts longer, this memory wall becomes AI’s biggest bottleneck.

But there’s a solution: intelligent model splitting. At WhaleFlux, we transform multi-GPU clusters into unified inference engines – like making 4x RTX 4090s (96GB total) outperform cloud solutions at 1/3 the cost. Let’s break down how to split LLMs without breaking your budget.

2. Why Splitting LLMs Across GPUs is Essential

The math is unavoidable:

Llama 3 400B: Requires ~800GB VRAM
Single H200: Only 141GB → You’ll need at least 6 GPUs

Splitting happens at three critical points:

Model weights (distributing layers)
KV cache (the real memory hog for long contexts)
Computation graphs (parallelizing operations)

WhaleFlux automates this complexity with topology-aware mapping for NVIDIA H100/H200 clusters, leveraging blazing-fast 3.2TB/s NVLink interconnects to minimize communication overhead.

3. KV Cache Partitioning: The Secret to Long-Context LLMs

KV cache consumes *70%+ of VRAM* in 128K-context scenarios. For a 70B model, that’s over 230GB! Here’s how partitioning solves it:

Technique	Pros	Cons
Tensor Parallelism	Lowest latency	Complex implementation
Sequence Chunking	Simple API	40% comms overhead
Hybrid Sharding	Best for WhaleFlux	Requires expert tuning

With WhaleFlux, hybrid sharding becomes turnkey:

python

# Distribute 128K-context KV cache across 4x H200s  
from whaleflux import KVCacheManager  
kv_manager = KVCacheManager(topology="hybrid_shard", gpus=4)

4. Step-by-Step: Splitting LLMs Across WhaleFlux Clusters

Phase 1: Model Segmentation

Vertical splitting: Assign layers to different GPUs
Horizontal splitting: Divide tensors across devices
WhaleFlux Tool: wf-analyze --model=mixtral-8x22b recommends optimal splits

Phase 2: KV Cache Distribution

Dynamically allocates attention heads
WhaleFlux Advantage: 78% lower transfer latency via InfiniBand RDMA

Phase 3: Load Balancing

Real-time monitoring of:

GPU memory pressure
Tensor core utilization
Inter-GPU bandwidth

5. Hardware Matters: GPU Selection for Efficient Splitting

Choose the right tools for your model size:

GPU Type	Max Model Size	WhaleFlux Monthly Lease
RTX 4090 (24GB)	30B params (2 GPUs)	$1,600
A100 (80GB)	180B params (3 GPUs)	$4,200
H200 (141GB)	400B+ params (6 GPUs)	$6,800

*All include NVLink bridges – 1-month minimum lease*

6. Performance Benchmarks: WhaleFlux vs. DIY

Testing Mixtral 8x22B inference (87K context):

Configuration	Tokens/sec	Latency	Cost Efficiency
8x A100 (Manual Split)	18.2	650ms	1.0x
8x H200 (WhaleFlux)	41.7	220ms	3.1x

*Key insight: WhaleFlux’s topology optimization reduces cross-GPU comms by 63%*

7. When Splitting Fails: Common Pitfalls & WhaleFlux Solutions

Pitfall 1: Network bottlenecks

Solution: WhaleFlux’s dedicated 400Gbps InfiniBand fabric

Pitfall 2: KV cache fragmentation

Solution: Unified virtual memory pooling

Pitfall 3: Load imbalance

Solution: Real-time telemetry with auto-rebalancing

8. Advanced: Dynamic Scaling with WhaleFlux Orchestrator

When context length suddenly jumps from 4K → 128K:

System detects VRAM pressure spike
Automatically provisions additional H200s (within 90 seconds)
Redistributes KV cache seamlessly
You pay only for scaled duration (1-month minimum)

9. Conclusion: Split Smart, Scale Fast

Splitting LLMs isn’t just a technical challenge – it’s economic optimization. WhaleFlux handles the complexity so you get:

3.9x higher throughput than public cloud
68% lower cost than DIY clusters
Zero implementation headaches

Stop wrestling with GPU limitations. Split intelligently, scale infinitely.

Splitting LLMs Across GPUs: Advanced Techniques to Scale AI Economically