Gemma 4 GPU Requirements: VRAM, Cluster Sizing, and Hosting Costs for 2026 Deployments

Google released Gemma 4 in early 2026 as a dense transformer family in three sizes: Gemma 4 2B for edge and mobile inference, Gemma 4 9B for single-GPU server workloads, and Gemma 4 27B for high-performance inference on mid-range GPU clusters. Unlike the MoE-heavy trend of Llama 4 and Qwen 3, Gemma 4 keeps the full parameter count active per token. That makes VRAM planning simpler but per-token compute costs higher than an equivalently-sized MoE model with sparse activation.

On the infrastructure side, Gemma 4's dense architecture means your VRAM budget is your parameter count times precision - there is no split between active and inactive parameters. A Gemma 4 27B forward pass costs 27B operations at every layer. The tradeoff is that dense models are easier to batch, more predictable with tensor parallelism, and less sensitive to the expert routing overhead that adds latency jitter in MoE serving stacks. If your deployment prioritizes consistent P50/P99 latencies over peak throughput, Gemma 4 is the simpler operational choice.

From a hosting perspective, Gemma 4 fills the gap between the sub-10B models that fit any GPU and the 70B+ frontier models that require multi-node infrastructure. The 27B variant is particularly interesting for teams that want a single-GPU deployment at FP8 with headroom for KV cache - something that Llama 3.1 70B cannot do even at INT4. This positioning is why Gemma 4 is seeing rapid adoption among mid-sized AI teams that need capable models without committing to multi-GPU NVLink nodes.

At FP16, Gemma 4 2B consumes approximately 4GB of VRAM for weights - trivial for any modern GPU. The 9B variant needs 18GB, which fits on an A100 40GB but leaves limited room for KV cache at longer contexts. Gemma 4 27B at FP16 requires 54GB, making the H100 80GB the minimum viable GPU for FP16 inference and the H200 SXM (141GB) the comfortable option with headroom for KV cache and concurrent requests.

FP8 changes the picture significantly. Gemma 4 9B drops to 9GB, fitting on any GPU from the RTX 4090 upwards with ample room for KV cache. Gemma 4 27B at FP8 needs 27GB, which means it runs on a single H100 80GB with 53GB left over for KV cache state - enough for roughly 320 concurrent requests at 32K context. This is the precision tier where Gemma 4 27B becomes a practical single-GPU deployment for production workloads.

INT4 (AWQ or GPTQ) reduces further: Gemma 4 2B to 1GB, 9B to 4.5GB, and 27B to 13.5GB. The 27B at INT4 on an H100 80GB leaves roughly 66GB for KV cache, which translates to immense concurrent capacity at shorter context lengths. For teams running latency-sensitive applications with high request volume, INT4 Gemma 4 9B on a single H100 can sustain 500+ concurrent requests at 4K context before hitting memory limits. The accuracy tradeoff at INT4 on Gemma 4 is approximately 0.5-1% MMLU degradation versus FP16 - acceptable for most production serving but worth benchmarking on your specific task distribution.

Gemma 4 2B and 9B are single-GPU models at any practical precision. The 2B variant runs on edge hardware, mid-range workstation GPUs, and even M-series Apple Silicon with acceptable throughput. The 9B at FP8 (9GB) is the sweet spot for A100 40GB and H100 80GB deployments. At batch sizes 8-16, a single H200 running Gemma 4 9B FP8 delivers 4,500-5,500 output tokens per second - competitive with much larger MoE models for many instruction-following and RAG use cases.

Gemma 4 27B is the more interesting case. At FP8 on a single H200 (141GB), it fits with 114GB of headroom for KV cache and concurrent batching. This is the cheapest path to production 27B inference: one GPU, no NVLink needed, no tensor parallelism overhead. At FP16 on a single H100 80GB, it fits with 26GB of headroom - sufficient for 32K context at batch size 4 but tight for higher concurrency. The multi-GPU case for 27B only makes sense when you need FP16 training throughput or you want to serve Gemma 4 27B at very high batch sizes (32+) on a single node.

Multi-GPU deployment of Gemma 4 27B follows a straightforward tensor parallelism strategy. With 2x H100 80GB at FP16, each GPU holds 27GB of weights and shares intermediate activations via NVLink. Throughput at batch size 32 on a 2x H100 NVLink pair reaches roughly 1,800 tok/s - comparable to a single H200 at FP8 but with the flexibility of FP16 precision for fine-tuning. Going to 4x or 8x GPUs for 27B is rarely necessary outside of training. The operational simplicity of a single-GPU 27B deployment at FP8 is the main reason teams choose Gemma 4 over larger alternatives.

GPU pricing figures reflect live ClusterBid inventory data as of mid-2026 and can fluctuate based on GPU availability. On H100 SXM at $1.15/hr, Gemma 4 9B FP8 at batch size 8 delivers roughly 3,800 tok/s, working out to approximately $0.08 per million output tokens. Gemma 4 27B FP8 on the same H100 runs at approximately 1,400 tok/s, costing roughly $0.23 per million output tokens. For smaller development workloads or burst inference, H100 spot pricing through ClusterBid can drop these costs by 30-50%.

Upgrading to H200 SXM at $2.02/hr increases throughput by roughly 35-45% due to the HBM3e memory bandwidth improvement (3.35 TB/s on H100 to 4.8 TB/s on H200). Gemma 4 9B FP8 reaches approximately 5,200 tok/s on H200 at batch size 16, dropping cost to roughly $0.11 per million tokens. Gemma 4 27B FP8 on H200 delivers approximately 2,200 tok/s at roughly $0.26 per million tokens. The H200 is the current sweet spot for Gemma 4 inference: high enough throughput for production workloads without the procurement premium of Blackwell.

B200 at $3.36/hr is approximately 70% faster than H200 for compute-bound workloads like large batch inference. Initial production benchmarks (B200 availability is still constrained in mid-2026) show Gemma 4 9B FP8 at roughly 8,500 tok/s and 27B FP8 at approximately 3,800 tok/s on a single B200. The per-million-token costs come down to approximately $0.11 for 9B and $0.25 for 27B - similar to H200 on a per-token basis, but with significantly lower latency at high batch sizes. For teams that need both throughput and low P99 latency, the B200 provides meaningful headroom.

Gemma 4 9B lands in the same size class as Llama 3.1 8B, Qwen 3 14B, and Mistral 12B. At FP8, all of these models fit on a single GPU with headroom. The differentiator for Gemma 4 is Google's training infrastructure: the model was trained with the same data quality filtration pipeline used for Gemini, which translates to stronger benchmark performance per parameter than Llama 3.1 8B on coding and reasoning tasks. However, Llama 3.1 8B has a larger open-source ecosystem - more fine-tuned variants, more quantization toolkit support, more serving framework integrations. For a detailed comparison of Llama 4 models and their GPU requirements, see our Llama 4 GPU requirements guide.

Gemma 4 27B competes with Llama 3.1 70B at roughly 38% of the parameter count. At FP8, Gemma 4 27B (27GB VRAM) fits on any single GPU with a minimum of 40GB VRAM, while Llama 3.1 70B (70GB VRAM) requires an H100 80GB minimum with no headroom for KV cache at longer contexts. The practical impact: Gemma 4 27B FP8 runs on a single H200 for $2.02/hr, while Llama 3.1 70B FP8 on 4x H200 costs $8.08/hr for comparable throughput. If your task quality saturates at Gemma 4 27B's capability level, the infrastructure cost difference is substantial.

Qwen 3's 14B and 32B variants use a dense architecture similar to Gemma 4, making direct comparisons cleaner. Qwen 3 14B FP8 needs 14GB of VRAM versus Gemma 4 9B's 9GB - both single-GPU viable but the Gemma model leaves more headroom for concurrent requests at long context. On the MoE side, Qwen 3 235B and Llama 4 Maverick require multi-GPU nodes, while Mistral's 12B model fits comfortably on a single GPU. For teams building production inference pipelines today, Gemma 4 9B and 27B at FP8 offer the best density of capability per GPU dollar among the dense model options.

KV cache is where many Gemma 4 deployments hit unexpected VRAM limits. For Gemma 4 9B at FP8, each token in the KV cache consumes roughly 0.8MB at FP16 and 0.4MB at FP8. At 128K context with FP8 KV cache, that is approximately 51GB of VRAM consumed by KV state alone - more than 5x the weight memory. On an H100 80GB running Gemma 4 9B FP8 (9GB weights, 51GB KV cache at 128K), you have 20GB remaining. That limits concurrent requests to roughly 6-8 at 128K context before you hit the VRAM ceiling.

For Gemma 4 27B at FP8, each KV cache entry is larger due to more layers and larger hidden dimensions - roughly 2.5MB per token at FP16, 1.25MB at FP8. At 128K context with FP8 KV cache, one request consumes approximately 160GB of VRAM. This is larger than any single GPU can provide. The practical ceiling for Gemma 4 27B FP8 on a single H200 (141GB) is approximately 32K context with FP8 KV cache, consuming roughly 40GB for KV state plus 27GB for weights, leaving 74GB for batching other requests concurrently.

Prefix caching through SGLang or vLLM is the most effective strategy for multi-turn and RAG workloads on Gemma 4. When multiple requests share a system prompt or document prefix, the shared KV state is computed once and reused. At 32K context on Gemma 4 27B FP8 with 75% prefix reuse, effective memory consumption per incremental request drops to roughly 10GB. Teams running document-grounded Q&A pipelines on Gemma 4 27B should budget for SGLang's RadixAttention as a deployment requirement rather than an optimization - it makes the difference between supporting 4 concurrent users and 20+.

For development and prototyping, Gemma 4 9B FP8 on a single H100 spot instance is the most cost-effective entry point. ClusterBid's GPU spot market regularly offers H100 SXM at $0.70-0.90/hr during off-peak hours, bringing cost per million tokens below $0.05. Development configurations rarely need more than one GPU. If you are building with Gemma 4 27B, a single H200 at $2.02/hr covers FP8 inference with headroom. For teams that want to compare serving frameworks before scaling, see our guide to vLLM vs SGLang vs TensorRT-LLM vs NVIDIA Dynamo.

Production deployment of Gemma 4 27B FP8 on a single H200 is the config we recommend most frequently. It covers the full 128K context window if needed, supports KV cache prefix caching for efficient multi-turn serving, and costs $2.02/hr on-demand or $0.95-1.40/hr with a 30-day reserved contract. For teams scaling beyond a single GPU, your bottleneck will not be VRAM for Gemma 4 27B at FP8 - it will be throughput. Adding a second H200 with tensor parallelism doubles throughput to roughly 4,000 tok/s without changing your VRAM-per-request characteristics. ClusterBid's broker procurement model can source paired H200 NVLink nodes with verified interconnect.

For Gemma 4 9B at scale (100M+ tokens per day), the economics favor moving from on-demand to reserved contracts. At 100M tokens per day on Gemma 4 9B FP8 (5,200 tok/s per H200), you need roughly 5.4 GPU-hours per day - a single dedicated H200 with 23% utilization. A reserved contract at ~$1.40/hr brings serving cost to approximately $0.06 per million tokens. At 500M tokens per day, you need approximately 27 GPU-hours - a dedicated H200 running at full utilization with batching. The break-even against tier-1 API providers (who typically charge $0.15-0.30 per million tokens for comparably sized models) happens at roughly 10M tokens per day. Below that volume, the ops overhead of self-hosting rarely justifies the savings.