Nobody warns you about the real problem with Llama 405B inference GPU setups until after it happens. The model does not run slow. It does not load at all. 405 billion parameters hit a memory wall and stop there. The GPU log shows an out-of-memory error before a single token is generated.
That is the experience teams hit every week.
DeepSeek inference requirements sit in similar territory. DeepSeek V3 carries 671 billion total parameters in a Mixture-of-Experts (MoE) design. Smart architecture, but the weights still need physical memory to live in.
This guide skips the vague cloud recommendations. It gives CTOs, ML engineers, and DevOps teams the specific numbers, real tradeoffs, and a clear path from model requirements to production hardware.
Also Read: Cloud GPU for Beginners: Complete Step-by-Step Guide 2026
Understanding Llama 405B & DeepSeek Requirements
Memory precision changes everything here. Start with the numbers.
What Are the GPU Memory Requirements Llama 405B?
Llama 405B at FP16 (half-precision) needs approximately 810 GB of VRAM to load weights. Add KV cache for long contexts and the total climbs past 1 TB. INT8 quantization brings the weight footprint to roughly 405 GB. INT4 compresses further, landing between 200 and 243 GB depending on the quantization method.
For comparison: a high-end gaming GPU carries 24 GB. You would need 34 of them for the weights alone.
| Model | Parameters | VRAM (FP16) | VRAM (INT8) | VRAM (INT4) |
| Llama 3.1 8B | 8B | 16 GB | 8 GB | 5 GB |
| Llama 3.1 70B | 70B | 140 GB | 70 GB | 40 GB |
| Llama 3.1 405B | 405B | 810 GB | 405 GB | 200–243 GB |
| DeepSeek V3 (MoE) | 671B total / 37B active | 1.3 TB | 650 GB | 400 GB |
Training vs inference: Training stores gradients and optimizer states on top of model weights, costing 2 to 3 times more memory than inference. This guide covers inference only.
DeepSeek’s architecture: DeepSeek V3 activates roughly 37 billion parameters per token through its MoE design. Per-token compute stays manageable. The catch: all expert weights stay in memory regardless, unless your serving runtime supports expert offloading. FP16 inference needs over 1.3 TB. INT4 lands around 400 GB.
Also Read: Blackwell GPU on Cloud in 2026: Should You Start Using It Now or Wait?
Top GPUs for the Best GPU for LLM Inference (Main Comparison)
Five GPUs appear consistently in real 405B and DeepSeek production setups. Here is what each one delivers.
NVIDIA H100 80GB
Specs: The specs include 80 GB HBM3 VRAM with 3.35 TB/s bandwidth, FP8 Transformer Engine, NVLink 4.0, 700W TDP.
HW FP8 support is only present in the mainstream GPU – the H100. The distinction can make a huge difference at the 405B scale. FP8 reduces weight memory by about half as compared to FP16 and increases throughput nearly double at a time. A single NVLink node handles Llama 405B at FP8, with 8 H100s. TensorRT-LLM supports H100 to achieve around 250-300 tokens per second with 70B-class models.
Scenario: 500 people supporting customers by using a chatbot. That’s what 8 H100s can do with room to expand and faster than real-time response times.
Strengths:
- Throughput is 2x to 2.8x higher than A100 for LLM workloads.
- FP8 has half as much memory as FP16 and achieves almost zero quality loss.
- NVLink 4.0 is a technology that enables GPU-to-GPU communication at 900 GB/s within a node.
- HBM3 bandwidth provides low latency with 128K context windows.
Weaknesses:
- New-market pricing – around $25,000 to $35,000 per unit.
- The 700W per card requirement requires specific power and cooling.
- If it is for a batch only, or low concurrency workload then this is an overkill.
NVIDIA H100 vs A100 for Llama: A100 80GB
Specs: 80 GB HBM2e VRAM, 2.0 TB/s bandwidth, NVLink 3.0, 400W TDP.
The A100 is the most popular production team. The total amount of VRAM is 640 GB, which is divided into eight A100s and utilizes Llama 405B at INT8. At least 12 are needed for FP16. Throughput of tokens is approximately 130 per second in 70B class models. The H100 outpaces it by 2 to 2.8x. The A100 rents 40 to 50 percent less per hour, making workloads with non-latency factors the cost difference disappear.
Scenario: 10,000 documents are summarized every night before 6AM. Nobody is waiting. Here it is about saving on cost per hour.
Strengths:
- 40 to 50% lower hourly cloud cost versus H100
- 80 GB VRAM handles large models with quantization applied
- Mature, stable software stack across all major frameworks
Weaknesses:
- No FP8 support; ceiling is BF16 or INT8
- Memory bandwidth of 2.0 TB/s vs H100’s 3.35 TB/s creates latency gaps at long context
- More GPUs required per 405B deployment than H100
NVIDIA L40S 48GB
Specs: The graphics card utilizes 48 GB GDDR6 VRAM with 864 GB/s of bandwidth, Ada Lovelace and has a TDP of 350W.
The L40S targets Llama 70B workloads on lean budgets. In large clusters of 16 or more units, it scales to 405B with heavy quantization. Hourly rental cost is the lowest of any data center GPU in this comparison. Two L40S cards running Llama 70B at INT4 undercut a single A100 on total cost.
Scenario: A SaaS team building a writing assistant on Llama 70B. Two L40S cards cover production traffic at a fraction of H100 pricing.
Strengths:
- Lowest hourly rate, approximately $0.87/hr on competitive cloud platforms
- PCIe form factor drops into standard server chassis
- Right fit for 70B inference on a disciplined budget
Weaknesses:
- GDDR6 bandwidth at 864 GB/s significantly trails HBM3 at 3.35 TB/s
- Requires aggressive quantization for 70B and above
- Single-card 405B inference is not practical
NVIDIA RTX 6000 Ada 48GB
Specs: 48 GB GDDR6 VRAM and workstation GPU with the Ada Lovelace architecture.
This card belongs in the development phase. The RTX 6000 Ada runs Llama 70B at INT4 without a data center. ML engineers testing prompts and configurations before production rollout eliminate the cloud bill during that phase. For 405B at any meaningful scale, it falls short.
Strengths: Workstation form factor, no data center required, accessible pricing for development use.
Weaknesses: Not suitable for production 405B inference, bandwidth limits throughput in scaled multi-GPU setups.
AMD MI300X 192GB
Specs: 192 GB HBM3 VRAM, 5.3 TB/s bandwidth, CDNA 3 architecture, 750W TDP, FP8 support.
The MI300X has 192 GB of VRAM per card, as opposed to any of the accelerators on this list. Eight units pool to 1.5 TB, enough for Llama 405B at FP8 on a single node. AMD’s own published research confirms MI300X deployments of Llama 3.1 405B and DeepSeek V3. For memory-bound workloads and long-context inference, it is genuinely competitive.
Scenario: A research lab needing full FP16 precision for 405B experiments. Fewer MI300X nodes are required than H100s because of the larger memory per card.
Strengths:
- Most VRAM of any mainstream accelerator available today
- 5.3 TB/s bandwidth outperforms H100 on memory-bound, long-output workloads
- Open-source ROCm stack with native PyTorch support
- Pricing competes with H100 in many regions
Weaknesses:
- Realized LLM throughput sits at approximately 37 to 66% of H100 in published benchmarks
- ROCm ecosystem has fewer optimized libraries than CUDA
- Multi-GPU collective communication bandwidth trails NVIDIA NVLink
Also Read: Cloud GPU Availability in 2026: Which GPUs Are Easy to Get Right Now?
Single vs Multi-GPU Setup Llama 405B
No single GPU runs Llama 405B at a usable precision. Multiple GPUs are mandatory. The real question is how many and how they connect.
Tensor parallelism splits model layers across GPUs. Each card processes a slice of every layer simultaneously and communicates results between forward passes. Latency stays low because no GPU waits idle.
NVLink makes this viable. H100 NVLink 4.0 is a bidirectional channel that carries 900 GB/s of data between one node. Its maximum performance is 64 GB/s and bottlenecks at 405B scale.
| Use Case | Recommended Setup |
| Prototyping Llama 70B | Single A100 80GB or H100 80GB |
| Internal tool, small team, 5s tolerance | 2x A100 or 2x L40S |
| Live chatbot, 100+ users, under 1s response | 4x H100 with NVLink |
| Llama 405B at FP8 | 8x H100, single NVLink node |
| Llama 405B at FP16 | Multi-node: 16+ H100 equivalent |
Traffic volume drives GPU count more than model size alone. A team serving 20 internal users on 405B at INT4 needs far fewer resources than a team serving 2,000 external users on 70B.
Also Read: Cloud GPU vs Owning GPUs 2026: Which Has Lower Cost?
Memory Optimization Techniques
More GPUs solve the problem. Smarter precision often solves it cheaper.
Quantization reduces numerical precision of model weights. FP32 stores 32 bits per value. INT4 uses 4. Fewer bits means smaller footprint and faster inference, with some precision trade-off.
| Format | Memory Reduction vs FP32 | Quality Trade-off | Speed Gain |
| FP16 | 50% | Minimal | Moderate |
| FP8 (H100 only) | 75% | Near-zero | High |
| INT8 | 75% | Low to moderate | High |
| INT4 / GPTQ | 87% | Moderate | Maximum |
Concrete example: Llama 405B at FP32 needs roughly 1.6 TB. At FP8, it falls to around 400 GB. At INT4, approximately 200 GB. Same model, dramatically smaller hardware requirement.
FP8 is the preferred starting point for H100 users. The Transformer Engine handles it natively with near-zero quality loss. This is why eight H100s cover 405B at FP8. Without FP8, you need significantly more cards.
INT8 and INT4 via GPTQ work on any GPU. AutoGPTQ and ExLlamaV2 are the most used implementations. Quality drops most noticeably at INT4 on math-heavy and structured reasoning outputs.
Flash Attention reduces VRAM during the attention computation step. Most production inference frameworks ship with it active by default.
Tools worth knowing:
- vLLM handles PagedAttention and continuous batching automatically
- Ollama runs locally with near-zero setup, designed for development
- TensorRT-LLM extracts maximum throughput from H100 hardware
- DeepSpeed manages tensor and pipeline parallelism across multi-node setups
Test quantized models thoroughly before production. Reasoning-heavy tasks show the sharpest quality drop at INT4.
Also Read: Single GPU or Multi-GPU Cloud: How to Know When It’s Time to Scale in 2026
Cost Analysis & ROI
Hourly rental rate or hardware sticker price is only part of the real cost.
| GPU | Cloud Rate (approx/hr) | New Unit Cost | Annual Power (8 hrs/day) | Per-Token (70B) |
| H100 80GB | $2.25–$2.40 | $25k–$35k | $730 | $0.45/1M tokens |
| A100 80GB | $1.35–$1.50 | $10k–$15k | $420 | $0.62/1M tokens |
| L40S 48GB | $0.87–$1.20 | $8k–$12k | $370 | Varies by model |
| AMD MI300X | $0.95–$3.41 | $15k–$25k | $730 | Competitive with H100 |
Rates shift frequently. Treat these as planning figures and verify directly with your provider.
Cloud rental removes upfront cost entirely. Right for startups, variable traffic, and teams still calibrating usage. For sustained 24/7 production, per-hour costs add up fast.
On-premises carries capital cost but pays back over time. An H100 running 8 hours per day typically breaks even against cloud rental within 6 to 8 months.
Hostrunway offers dedicated GPU servers powered by NVIDIA H100, H200 and B200 in 160+ locations in 60+ countries to teams that require GPU infrastructure globally but do not require time to provision. No lock-out period, which means that you grow when traffic calls for it — not when a contract says you can. Human support is available 24-hours a day and responds within 15 minutes. When production inference goes down at 2 AM, that response time matters.
Also Read: Cloud GPU for AI Inference vs Training: Different Needs Explained
Benchmarks & Real-Time LLM Inference Performance Data
These figures come from published benchmarks. Real results vary by batch size, context length, quantization, and framework version.
| GPU Setup | Model | Batch = 1 | Batch = 32 |
| Single H100 80GB | Llama 70B (FP8) | 115 tok/s | 250–300 tok/s |
| Single A100 80GB | Llama 70B (BF16) | 78 tok/s | 130 tok/s |
| Single L40S 48GB | Llama 70B (INT4) | 25–40 tok/s | 80–120 tok/s |
| 8x H100 Node | Llama 405B (FP8) | 50–80 tok/s | 300–400 tok/s |
| 8x A100 Node | Llama 405B (INT8) | 20–35 tok/s | 120–180 tok/s |
Latency measures milliseconds to the first token. Chatbots, copilots, and coding assistants with a human waiting on a response: this is the number that defines the product experience. H100’s 3.35 TB/s bandwidth holds latency steady even at 128K context lengths where A100 starts to degrade under load.
Throughput measures total tokens per second across all concurrent requests. Document pipelines, batch summarization jobs, overnight processing: this drives cost efficiency.
H100 is 2-2.8 times the throughput of A100. It is often more expensive per hour, but can be cheaper per token at high traffic rates.
Also Read: Docker or Bare Metal on Cloud GPU? How to Choose the Right One in 2026
Deployment Considerations (Production Setup)
Getting the GPU right is step one. The surrounding infrastructure determines whether production stays up.
Inference frameworks to know:
- vLLM: Production default. PagedAttention, continuous batching, H100 FP8 from version 0.4+.
- Ollama: Single-command local setup for development. Not built for high-concurrency production.
- TensorRT-LLM: Maximum tokens-per-second from H100. More setup, better peak throughput.
- DeepSpeed: Built for multi-node distributed inference at 405B scale.
Power: Each H100 draws 700W. A full 8-GPU server peaks near 7 kW. Dedicated circuits, proper amperage, and redundant UPS are required.
Cooling: H100 clusters generate heat that standard server room air conditioning handles poorly. Data center CRAC units or liquid cooling are the practical answer.
Network: NVLink handles intra-node GPU communication. Multi-node setups require InfiniBand or high bandwidth Ethernet. Attach them to a computer with less RAM than the interconnect requires and it will be the limiting factor before reaching the GPU compute.
Failover: One failed GPU in a tensor-parallel setup takes down the whole inference instance. Kubernetes-based orchestration handles automatic replacement. Load balancers across multiple inference instances maintain 99.9% uptime targets.
Load test in staging before going live. Bottlenecks found there cost nothing. Bottlenecks found in production cost users.
Also Read: Spot vs On-Demand vs Reserved Cloud GPUs: Which Pricing Model Saves You More in 2026?
Recommendations & Decision Framework
Three questions narrow the choice.
Response time under 1 second required? Yes: H100. No: A100 covers most production workloads.
Total budget under $20K? Yes: L40S cluster or cloud rental on A100. No: H100 or A100 cluster, dedicated or on-premises.
24/7 production traffic? Yes: On-premises or dedicated infrastructure with redundancy. No: Hourly cloud rental scaled down when idle.
GPU Recommendations at a Glance
| Use Case | Best Choice |
| Llama 405B full production | NVIDIA H100 80GB (8-GPU NVLink node) |
| Budget 70B inference | NVIDIA A100 80GB or L40S cluster |
| Startup or early-stage team | Cloud rental on A100 or H100 |
| Research with memory constraints | AMD MI300X 192GB |
| Development and testing | NVIDIA RTX 6000 Ada 48GB |
Pre-Purchase Checklist
Latency under 1 second needed? H100.
Running 24/7 at scale? On-premises math often beats cloud.
Lowest total cost of ownership? A100 with INT8 quantization.
[Still in the testing phase? Cloud rental avoids upfront commitment.
Running 405B at FP8? Minimum 8x H100 on a NVLink node.
Memory is the hard constraint? AMD MI300X 192GB.
Under $20K budget? L40S cluster or cloud rental.
Frequently Asked Questions (FAQs)
Can I run Llama 405B on a single 80GB GPU?
No. Llama 405B at FP16 requires roughly 810 GB of VRAM. The footprint for INT4 is 200-243 GB. They’re not going to be satisfied with a single card that holds 80 GB. Minimum practical setup for FP8 inference is 8x H100 on a NVLink node.
What is the difference between H100 and A100 for Llama inference?
H100 delivers 2 to 2.8 times the throughput of A100 on LLM workloads. The primary driver is memory bandwidth: HBM3 at 3.35 TB/s versus HBM2e at 2.0 TB/s. Native FP8 support on H100 widens the gap further for large models. H100 rents for more per hour but often costs less per token at high traffic volumes.
Is it cheaper to rent GPUs or buy them outright?
Cloud rental wins for variable workloads and early-stage teams with no committed usage. Buying wins for sustained 24/7 production. An H100 at 8 hours of daily use typically breaks even against cloud rental within 6 to 8 months.
Does quantization hurt Llama 405B output quality?
FP8 on H100 introduces nearly no detectable quality loss. INT8 causes slight degradation, most visible in math and step-by-step reasoning. INT4 degrades more noticeably. Benchmark your specific use case before committing a quantization level to production.
Can I run DeepSeek inference requirements on the same GPUs as Llama 405B?
Yes. DeepSeek V3 runs on the same hardware. Its MoE architecture activates roughly 37B parameters per token, keeping per-token compute comparable to a smaller dense model. Total weight memory stays large at full precision. The same 8x H100 or equivalent MI300X setup that handles Llama 405B works for DeepSeek with quantization adjusted for your precision target.
What is the most cost-effective LLM inference GPU for a startup?
Cloud rental on A100 nodes is the lowest-risk entry point. No upfront commitment, scale to zero when traffic stops. If the production model is Llama 70B rather than 405B, two A100 80GB cards on a dedicated server handle most startup workloads at a manageable monthly cost.
Do H100s need special power and cooling?
Yes. Each H100 draws 700W. An 8-card server peaks near 7 kW. Standard office server rooms lack the power density and cooling capacity for this. Data center infrastructure is required: dedicated circuits, CRAC units or liquid cooling, and UPS systems rated for sustained GPU load.
