The hardware question isn’t “GPU or no GPU.” It’s VRAM configuration, how much power it draws under sustained load, whether your cooling can handle it, and whether your storage can keep up with your dataset.
The hardware that handles your fine-tuning workflow is not the same hardware that handles production inference. And neither is the same as what you need at the edge. We build around the actual constraints — not the category.
Tell us your model, your stack, and where it needs to run. We’ll build what it actually needs.
Running Llama 4 Maverick or Scout across a multi-GPU cluster? The bottleneck isn’t the model — it’s HBM3 memory bandwidth, NVLink vs InfiniBand interconnect topology, and whether your rack can sustain 120kW without a liquid cooling overhaul. We spec, build, and validate the full infrastructure stack for your model configuration before anything ships.
LLAMA 4 SCOUT
Llama 4 Maverick
Llama 3.1 70B
Llama 3.1 70B
Mixtral 8x22B
Qwen3 72B
Custom fine-tunes
HBM3 bandwidth
NVLink / InfiniBand
Liquid cooling at density
Multi-node interconnect
Power at 120kW+/rack
Storage I/O for training data
A fine-tuned Phi-4-mini or Qwen3-1.7B quantized to Q4_K_M running on llama.cpp will outperform a 70B cloud model on your specific task — at a fraction of the cost, with zero latency, and no data leaving the building. The constraint is building the inference node that fits the physical envelope: sub-200W, 8GB VRAM, ruggedized for the actual operating environment.
Phi-4-mini 3.8B
Qwen3 0.6B–4B
Llama 3.2 3B
Gemma 3n
Ministral-3B
GGUF Q4_K_M
8–16GB VRAM config
Sub-200W TDP
Ruggedized chassis
llama.cpp / Ollama validated
Thermal at 95% humidity
No cloud dependency
Fine-tuning a Llama 3.1 8B on your domain data via HuggingFace Transformers needs a different machine than running inference with Ollama. Dataset NVMe throughput, GPU memory bandwidth for LoRA fine-tuning, and the CUDA stack all matter. A catalog workstation with a consumer GPU will bottleneck your fine-tune. We build for the actual workflow.
HuggingFace Transformers
Ollama local inference
LoRA / QLoRA fine-tuning
MLX (Apple Silicon)
ExecuTorch
vLLM serving
8–16GB VRAM config
Sub-200W TDP
Ruggedized chassis
llama.cpp / Ollama validated
Thermal at 95% humidity
No cloud dependency
Ollama
llama.cpp
vLLM
HuggingFace Transformers
GGUF / Q4_K_M
ExecuTorch
MLX
CUDA / TensorRT
PyTorch
speculative decoding
Supply chain constraints are real. GPU lead times are running 6 to 12 months. Power infrastructure at most facilities wasn’t designed for modern AI density. For a significant portion of the market, retrofitting and reusing existing hardware isn’t a compromise — it’s the only viable path. Upgrading the GPUs in an existing rack. Converting air-cooled infrastructure to liquid cooling. Extending the life of existing HPC nodes with memory and interconnect upgrades. The Tier-1 OEM won’t touch hardware they didn’t sell. Equus was built for exactly this work. And for active clients, we bring 35 years of supply chain relationships to the sourcing challenge — helping navigate component allocation and lead time constraints that stop organizations buying through standard channels. It’s never been more needed than right now.
Pull out aging GPUs, install current-generation accelerators, confirm the existing chassis can handle the heat and power draw. When new GPU lead times are 6 to 12 months, retrofitting existing hardware is frequently faster and more cost-effective than waiting for new inventory.
“Zero new procurement, 4x inference throughput. And when we did need new components, Equus sourced them in three weeks through relationships we couldn’t have accessed ourselves.”
Air-cooled servers hitting thermal limits under GPU-dense model workloads. We architect direct-to-chip or rear-door liquid cooling into existing racks without replacing the infrastructure you already paid for.
“Power draw down 30%, density doubled.”
Adding HBM or NVMe to existing HPC nodes to meet model inference memory bandwidth requirements. Retrofitting InfiniBand or 400G ethernet for multi-GPU training. The compute was there — the interconnect was the problem.
“Diagnosed and resolved in two weeks.”
Most existing data centers were designed for 10kW racks. Modern GPU clusters pull 40 to 120kW. Before committing to new hardware, the facility has to be able to run it. We assess whether your facility can actually support it, architect the upgrades, and execute — so you’re not ordering hardware for a facility that can’t support it.
“We knew what we wanted to buy. Equus told us the building couldn’t support it — and fixed that first.”
Not sure what your existing infrastructure needs? We assess the full stack — compute, cooling, power, networking, storage — and tell you exactly what to upgrade, what to replace, and what to leave alone.
“They told us what NOT to upgrade — and what to focus on first.”
“The Tier-1 OEM said our existing infrastructure couldn’t support the model workload. Equus came in, assessed it, and had us running in six weeks. No new servers.”
Tell us your model, your quantization, your serving framework, and where it needs to run. We’ll tell you exactly what hardware it needs — and validate it before it ships.
We are the hardware layer beneath your software product.
Deploy into constrained environments, hospitals to factory floors.
Large-scale inference and sovereign data centers.
