AI Inference Infrastructure Supplier — GPU Servers for LLM and AI Model Deployment

Haink supplies AI inference infrastructure to enterprises deploying large language models, image generation, and AI APIs at production scale across Hong Kong, Dubai, UAE, and Mainland China. AI inference infrastructure — the GPU servers and networking that run trained models and serve responses to users — differs fundamentally from training infrastructure in its optimization priorities: inference favors GPU memory capacity, throughput per dollar, and power efficiency over raw FLOPS. Haink sources inference-optimized GPU platforms from NVIDIA, Supermicro, Dell, and HPE.

Training vs Inference: Different Hardware Problems

AI training and AI inference are distinct workloads with different hardware requirements. Training involves computing gradients across billions of parameters repeatedly over millions of steps — it is heavily compute-bound and benefits from the highest possible FLOPS and GPU-to-GPU communication bandwidth (NVLink, InfiniBand). Inference involves loading a fixed model into GPU memory and running forward passes for user queries — it is primarily memory-capacity-bound (the entire model must fit in GPU VRAM) and latency-sensitive (each user query must return in milliseconds to seconds). This distinction drives different hardware choices for inference versus training.

GPU Options for AI Inference

NVIDIA H100 SXM5 — High-Throughput Inference

H100 SXM5 with 80 GB HBM2e and 3.35 TB/s memory bandwidth is the most capable inference GPU for models that fit in 80 GB. It supports TensorRT-LLM FP8 inference at 3,958 TFLOPS, enabling high-concurrency serving of 7B–34B models. For 70B models, H100 SXM5 requires tensor parallelism across two GPUs (2× 80 GB = 160 GB for FP16, or single GPU for INT4 quantized). H100 SXM5 is typically used in inference when a cluster built for training is also used for serving — the same 8-GPU SXM server runs training overnight and inference during business hours.

NVIDIA H200 SXM5 — Best for 70B+ Inference

H200's 141 GB HBM3e per GPU is the most impactful upgrade from H100 specifically for inference of large models. A single H200 GPU serves a 70B model at FP16 without tensor parallelism (70B × 2 bytes = ~140 GB), whereas H100 requires two GPUs for the same model. This halves the GPU count — and thus infrastructure cost and power — for 70B inference deployments. H200 also benefits long-context inference (128K+ token context windows) where KV cache memory becomes the binding constraint. For enterprises primarily deploying inference rather than training, H200 PCIe NVL in a standard server is often more cost-efficient than H100 SXM5.

NVIDIA L40S — Cost-Optimized Inference

NVIDIA L40S (48 GB GDDR6 ECC, 362 TFLOPS FP8, Ada Lovelace architecture) is Haink's primary recommendation for dedicated inference infrastructure where training is not a requirement. L40S does not require liquid cooling, runs in any standard PCIe server rack slot, and costs substantially less per GPU than H100. A 2U server with four L40S GPUs (192 GB total VRAM) serves 70B models at 4-bit quantization (INT4) across four cards with adequate throughput for small-to-medium production APIs. For enterprises running 7B–34B models for internal AI assistants, L40S provides the best throughput-per-dollar for inference-only deployments. Supermicro SYS-221GE (4× L40S) and SYS-111E (2× L40S) are the primary server platforms.

NVIDIA B200 SXM5 — Maximum Inference Throughput

B200's 192 GB HBM3e and FP4 precision (18,000 TFLOPS) make it the highest-throughput inference GPU currently available. FP4 inference enables running 70B models on a single B200 with minimal quality degradation versus FP16, while delivering 2× more throughput than FP8. For enterprises serving high concurrency (thousands of simultaneous API calls), B200-based inference infrastructure reduces the GPU count needed to sustain a given throughput target by 2–3× compared to H100. B200 requires direct liquid cooling — the infrastructure investment required limits its use to organizations with DLC-capable data centers or the workloads that justify the capability premium.

NVIDIA B300 SXM — Frontier Model Inference

B300 (Blackwell Ultra) with 288 GB HBM3e is optimized for serving the largest deployed models (405B, 671B) at production scale. Its 288 GB per GPU enables tensor-parallel inference of 405B models across fewer GPUs than H100 or H200 — and at significantly higher throughput. B300 targets organizations deploying frontier models at scale where GPU count reduction per served token directly translates to infrastructure cost savings.

Inference Serving Architectures

Single-Model Dedicated Server

For enterprises running one primary AI model (internal LLM assistant, document processing pipeline, code generation service), a dedicated inference server with 2–8 GPUs sized to hold the model comfortably with room for KV cache is the simplest architecture. A Supermicro SYS-111E with 2× L40S (96 GB) serves a 70B INT4 model plus KV cache for moderate concurrency. A single Dell PowerEdge R760xa with 4× H100 PCIe (320 GB) serves a 70B FP16 model at production throughput.

Inference Cluster with Load Balancing

For high-concurrency production APIs, multiple inference servers behind a load balancer distribute query load across GPU replicas. Each server holds a full model replica; the load balancer routes requests to the server with available capacity. This horizontal scaling approach is straightforward and resilient — individual server failures do not take down the service. vLLM, TGI (Text Generation Inference), and NVIDIA TensorRT-LLM are the primary inference serving frameworks running on each node.

Multi-Node Tensor Parallel Inference

For the largest models (405B+) that cannot fit on a single server's GPU memory, tensor parallelism splits the model across multiple servers with InfiniBand or RoCE providing the required inter-server communication bandwidth. A 405B model at FP16 requires 810 GB — fitting across 5–6 H200 GPUs or 3 B300 GPUs. Multi-node inference requires careful latency management: the InfiniBand fabric must be low-latency enough that inter-GPU communication does not dominate total inference time.

Inference Performance: Throughput vs Latency

AI inference optimization involves a fundamental trade-off between throughput (tokens per second across all concurrent users) and latency (time to first token and time per output token for each individual request). Batching multiple user requests together and processing them simultaneously improves throughput but increases latency for individual requests. GPU memory bandwidth — not raw FLOPS — is typically the throughput bottleneck for autoregressive LLM inference, which is why H200 (4.8 TB/s) outperforms H100 (3.35 TB/s) for inference even though both have the same FP8 TFLOPS.

Inference Infrastructure for Common Workloads

For a 7B parameter model (Llama 3.1 7B, Qwen 2.5 7B) at FP16: fits in 14 GB GPU memory — a single NVIDIA L40S (48 GB) serves this model with room for very large KV caches. For a 34B model at FP16: fits in 68 GB — a single H100 SXM5 (80 GB) or H100 PCIe (80 GB) with sufficient headroom. For a 70B model at FP16: requires 140 GB — a single H200 (141 GB) or two H100s; at INT4: 35 GB, fits on a single L40S. For a 405B model at FP16: requires 810 GB — minimum 6 H200 GPUs or 3 B300 GPUs in a tensor-parallel configuration.

Haink Inference Infrastructure Supply

Haink sources and delivers AI inference server platforms from NVIDIA, Supermicro, Dell, and HPE to enterprise data centers in Hong Kong, Dubai, and Mainland China. Inference server configurations are matched to the specific model size, concurrency target, latency requirement, and budget of each deployment. For organizations transitioning from cloud AI inference to private on-premise inference, Haink provides guidance on hardware selection and total cost of ownership versus cloud API pricing.

Related Resources

Frequently Asked Questions

What GPU is best for AI inference in 2026?

For cost-optimized inference of 7B–34B models: NVIDIA L40S (48 GB, no liquid cooling required). For inference of 70B models on fewer GPUs: NVIDIA H200 (141 GB per GPU). For maximum throughput on any model size: NVIDIA B200 or B300. For teams where the same hardware needs to do both training and inference: NVIDIA H100 or H200 SXM5 in an 8-GPU server. The right answer depends on model size, concurrency target, power infrastructure, and budget.

How many GPUs do I need to serve a 70B model in production?

With H100 PCIe (80 GB each): 2 GPUs minimum for FP16, 1 GPU for INT4 quantization. With H200 (141 GB): 1 GPU at FP16 with overhead for KV cache. With L40S (48 GB each): 2 GPUs at INT4 quantization. For production at meaningful concurrency (50+ simultaneous users), multiply the minimum by 2–4× to maintain acceptable per-request latency under load, and consider multiple server replicas behind a load balancer for high availability.

Is it cheaper to use cloud inference APIs or private inference infrastructure?

At low usage (under 100,000 tokens/day), cloud APIs (OpenAI, Anthropic, AWS Bedrock) are cheaper — no infrastructure cost to amortize. At production scale (millions of tokens per day), private inference infrastructure typically costs 5–15× less per token than cloud APIs after amortizing hardware cost over 3 years. The crossover point depends on model, token volume, and hardware cost — Haink can provide a cost comparison for specific use cases.