INNO3D Tower Workstation AWS-511Z-R1

The INNO3D Tower Workstation AWS-511Z-R1 is a full tower, single-socket Xeon W platform designed to hold up to four workstation-class Blackwell GPUs. It sits in the professional workstation tier, above consumer desktops and below rack-mounted data center servers, and it exists for one reason: putting 96 GB to 192 GB of GPU memory on or under a desk without renting cloud capacity.

INNO3D is best known for graphics cards, and this machine reads like a chassis built around that expertise. The W790 board takes one Intel Xeon W-2400, W-2500, W-3400, or W-3500 processor on LGA4677, with 16 DIMM slots and a 2 TB DDR5 3DS RDIMM ceiling on the W-3400/W-3500 parts (1 TB on W-2400/W-2500). Six PCIe Gen 5 x16 slots (one wired at x8) mean the GPU layout is a real choice rather than a fixed configuration: four RTX PRO 4500 Blackwell Workstation Edition cards at 32 GB each, or two RTX PRO 6000 Blackwell Workstation Edition cards at 96 GB each.

For anyone hunting for the best hardware for running AI models locally, the interesting number here is not core count. It is VRAM. A 70B parameter model at Q4 needs roughly 40 GB. At FP8 it needs about 70 GB. At FP16 it needs 140 GB. The AWS-511Z-R1 is one of the few tower platforms that clears all three of those bars without offloading weights to system RAM, where token generation collapses to single-digit throughput.

Inference speed on a dense transformer is memory-bandwidth bound, not compute bound. Every token generated requires reading the full weight set once, so tokens per second scales roughly with bandwidth divided by model size.

• 2 x RTX PRO 6000 Blackwell Workstation Edition: 96 GB GDDR7 per card, roughly 1.8 TB/s per card. Total 192 GB of VRAM and about 3.6 TB/s of aggregate bandwidth. This is the configuration that matters for serious local LLM work.
• 4 x RTX PRO 4500 Blackwell Workstation Edition: 32 GB per card, roughly 0.9 TB/s each. Total 128 GB and about 3.6 TB/s aggregate. More total memory than a single PRO 6000, but sharded across four devices, which raises interconnect overhead and constrains how you split a model.
• CPU and system memory: W-3400/W-3500 chips run 8-channel DDR5, W-2400/W-2500 run 4-channel. With 16 DIMM slots, the platform can hold enough system RAM to host a full unquantized checkpoint for CPU-assisted loading, though CPU inference itself will be an order of magnitude slower than GPU.
• Storage: two PCIe Gen 5 x4 M.2 slots plus one Gen 3 x4 M.2 slot, and up to 12 front-accessible 3.5"/2.5" bays for SATA, SAS, or NVMe. Model weights are large; fast local NVMe matters more than most buyers expect.
• Networking: 10GbE RJ-45, 1GbE RJ-45, and a dedicated 1GbE BMC management port. The BMC is what separates this from a gaming tower: you can manage and monitor it headless.
• Power: dual 3000 W CRPS 80 Plus Titanium redundant units (1+1). Two fully loaded PRO 6000 cards will pull well over 1000 W under sustained inference load, so budget for a 208-240 V circuit. A 120 V/15 A wall outlet will not carry this machine.

GPUs are sold separately and INNO3D has not published pricing or a system-level MSRP. Note also that INNO3D's own product literature states no GPU or PSU is included with the base product, so confirm what the reseller actually ships before ordering.

Estimates below assume batch size 1, a modern runtime (vLLM, TensorRT-LLM, or llama.cpp), and reasonable context lengths. Real numbers vary with quantization format, KV cache pressure, and concurrency.

Dual RTX PRO 6000 (192 GB)

• Llama 3.1 8B / Mistral 7B: trivial. Q4 fits in under 6 GB. Expect 300+ tokens per second, effectively limited by your client.
• Llama 3.3 70B / Qwen 2.5 72B: the sweet spot. Q4_K_M or AWQ lands around 40 GB and runs at roughly 60-80 tokens per second. FP8 at ~70 GB runs 40-50 tokens per second with noticeably better quality. FP16 at 140 GB fits and runs, but leaves little room for KV cache.
• Mixtral 8x22B: Q4 around 80 GB, comfortably inside one card. MoE architectures generate faster than their parameter count suggests because only active experts are read per token.
• Mistral Large 123B / Command R+ 104B: Q4 at 70-75 GB, 40-50 tokens per second.
• DeepSeek-R1 671B: the full model at Q4 needs roughly 400 GB. It does not fit. You can run it with expert offloading to system RAM, but throughput drops to low single digits. Distilled variants (R1-Distill-Llama-70B, R1-Distill-Qwen-32B) run well.
• Llama 3.1 405B: Q4 is approximately 230 GB, still beyond 192 GB. Partial offload only.

The 192 GB configuration is the point where a local LLM stops feeling like a demo. With 70B at FP8 you retain well over 100 GB for KV cache, which is what enables long-context work and multiple concurrent agent sessions without eviction thrash.

4 x RTX PRO 4500 (128 GB)

• 70B at Q4: 50-70 tokens per second with tensor parallelism across four cards.
• 70B at FP8: fits at ~70 GB, 35-45 tokens per second.
• Multimodal: Qwen2.5-VL 72B and Llama 3.2 Vision run at Q4/Q8, though image tokens consume KV cache quickly.
• Long context: 128K-token contexts on a 70B model are feasible at Q4, but four-way sharding adds communication overhead per token.

Quantization guidance: on Blackwell, FP8 is the quality-to-speed sweet spot for models that fit. It is natively supported, halves memory versus FP16, and costs far less accuracy than 4-bit. Drop to Q4_K_M or AWQ only when you need the headroom for context or concurrency.

• Teams running local inference servers: the 10GbE port, BMC, redundant Titanium PSUs, and 12 drive bays are server features in a tower chassis. This is the right shape for an on-prem inference endpoint that does not need a rack.
• Developers building agentic workflows: multiple concurrent agent sessions against a 70B model at FP8 is exactly what the 192 GB configuration is sized for. This is a credible answer to the question of the best hardware for local AI agents in 2026.
• Engineering and digital twin work: INNO3D positions this for simulation, 3D rendering, and Omniverse workflows. The Xeon W's 8-channel memory and ECC RDIMM support matter for those workloads.
• Hobbyists and researchers: possible, but this is a large, loud, power-hungry machine. A single-GPU desktop is a better starting point unless you specifically need more than 48 GB of VRAM.
• Training: fine-tuning 7B-13B models with LoRA is practical. Full fine-tuning of 70B is not the intended use; that is data center territory.

Dell Precision 7960 Tower: dual-socket Xeon Scalable, up to four GPUs, similar memory ceiling. Dell offers better enterprise support, validated driver stacks, and global service contracts. INNO3D counters with a single-socket design that avoids NUMA complexity and a 3000 W redundant power configuration that is unusual in a tower. Pick Dell if procurement and support matter more than raw configuration flexibility.

A self-built 4 x RTX 5090 rig: 32 GB per card gives 128 GB total for substantially less money, and the consumer cards have higher per-GPU bandwidth than the RTX PRO 4500. What you give up is ECC memory, blower-style cooling designed for multi-GPU density, ISV certification, a BMC, and any vendor warranty covering the system as a whole. For a lab or a startup that can tolerate that, it is the cheaper path to 128 GB.

Choose the AWS-511Z-R1 when you need 192 GB of VRAM in a single tower with redundant power, remote management, and a validated chassis that will not thermal-throttle four cards under sustained load. If your models fit in 48 GB, this is more machine than you need.